-
Open Access Article
1 - Predicting local scour depth of bridge piers using hybrid particle swarm optimization and gray wolf optimizer
Mehran Sarabi Seyed Abbas HosseiniConstruction of bridge piers is expensive, and scouring near them can lead to instability. Without a suitable solution, it can ultimately result in the structure’s destruction. Therefore, a detailed study is required to understand this phenomenon and the factors affecti MoreConstruction of bridge piers is expensive, and scouring near them can lead to instability. Without a suitable solution, it can ultimately result in the structure’s destruction. Therefore, a detailed study is required to understand this phenomenon and the factors affecting it. This research entails utilizing extensive field data to measure the local scour depth around bridge piers. It proposes an equation comprising scour-affecting parameters and defines an optimization model to establish this relationship. The decision variables of this model were determined using a meta-heuristic algorithm called the hybrid gray wolf-particle swarm (HPSGWO). For this purpose, various relationships were established to ascertain scour depth, and subsequently, the local scour depth of the bridge piers was calculated, based on these equations. Root Mean Square Error (RMSE), Relative Square Root (RSR), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Correlation Coefficient (CC) were employed as error measurement indices to evaluate the relationships. Upon comparison of the error measurement indices for the obtained relationships, the best input parameter combination and mathematical relationship for calculating scour depth were determined. These indices for the superior model are equal to 0.504 m, 0.52, 0.73, 7.7%, and 0.734 for RMSE, RSR, NSE, PBIAS, and CC, respectively. These values show that the equation presented in this research is suitable for calculating scour depth and is more reliable than the presented experimental methods. In the proposed relationship, scour depth is directly proportional to the Froude number and the ratio of base width to water depth while inversely proportional to the average size of bed particles to water depth. Manuscript profile -
Open Access Article
2 - Introducing a new meta-heuristic algorithm to solve the feature selection problem
Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi HafshejaniDue to the increase in the volume of data and information in recent years, the issue of choosing the most appropriate feature for decision making has become very important. Classic attribute selection methods cannot work well on big data. Because feature selection is a MoreDue to the increase in the volume of data and information in recent years, the issue of choosing the most appropriate feature for decision making has become very important. Classic attribute selection methods cannot work well on big data. Because feature selection is a complex problem, it seems appropriate to use meta-heuristic algorithms to solve this problem. In this paper, a new meta-heuristic algorithm inspired by nomadic migration to solve the feature selection problem is presented. This algorithm is named in honor of the Qashqai tribe. In this hybrid algorithm, the proportional function was designed based on the feature selection algorithm and based on minimizing the number of features and the amount of data error using neural network results. Then the Qashqai meta-heuristic algorithm was implemented on this fitness function and the results were compared with the well-known meta-heuristic algorithms of genetics and particle swarm. The results of the hypothesis test showed that the Qashqai optimization algorithm to solve the feature selection problem by the genetic algorithm and particle swarm is not defeated and in terms of convergence to the optimal solution works well. Manuscript profile -
Open Access Article
3 - A New Architecture Based on Artificial Neural Network and PSO Algorithm for Estimating Software Development Effort
Amin Moradbeiky Amid Khatibi Bardsiri -
Open Access Article
4 - A New Shuffled Sub-swarm Particle Swarm Optimization Algorithm for Speech Enhancement
Masoud Geravanchizadeh Sina Ghalami Osgouei -
Open Access Article
5 - Solving random inverse heat conduction problems using PSO and genetic algorithms
I. Hossein Zade Shahbolaghi R. Pourgholi H. Dana Mazraeh S.H. TabasiThe main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solvin MoreThe main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorithm and the genetic algorithm, we solve them. The algorithms presented in this article have advantages over other old methods that have been presented so far. Implementing these algorithms is simpler, have less run time and produce better approximation. The numerical results obtained in this paper also show that the solutions obtained for the examples presented in the numerical results section are highly accurate and have less error. All of the algorithms in this paper to obtain the desired numeric results, have been implemented on the Pentium (R) Dual core E5700 processor at 3.00 GHz. Manuscript profile -
Open Access Article
6 - Synchronization of a Heart Delay Model with Using CPSO Algorithm in Presence of Unknown Parameters
S. Nazari A. HeydariHeart chaotic system and the ability of particle swarm optimization (PSO) method motivated us to benefit the method of chaotic particle swarm optimization (CPSO) to synchronize the heart three-oscillator model. It can be a suitable algorithm for strengthening the contro MoreHeart chaotic system and the ability of particle swarm optimization (PSO) method motivated us to benefit the method of chaotic particle swarm optimization (CPSO) to synchronize the heart three-oscillator model. It can be a suitable algorithm for strengthening the controller in presence of unknown parameters. In this paper we apply adaptive control (AC) on heart delay model, also examine the system stability by the Lyapunov stability theorem. Then we improve results with using CPSO algorithm and define an appropriate cost function. At the end of we implement the proposed approach on an example. Manuscript profile -
Open Access Article
7 - Comparison of particle swarm optimization and tabu search algorithms for portfolio selection problem
M. Kazemi A. Heidari M. LashkaryUsing Metaheuristics models and Evolutionary Algorithms for solving portfolio problem has been considered in recent years.In this study, by using particles swarm optimization and tabu search algorithms we optimized two-sided risk measures . A standard exact penalty func MoreUsing Metaheuristics models and Evolutionary Algorithms for solving portfolio problem has been considered in recent years.In this study, by using particles swarm optimization and tabu search algorithms we optimized two-sided risk measures . A standard exact penalty function transforms the considered portfolio selection problem into an equivalent unconstrained minimization problem. And in finally the historical data from s&p100 from years 2007 through 2009 is used as model input and then the model was solved and these algorithms were compared. Manuscript profile -
Open Access Article
8 - Selection and Portfolio Optimization by Mean–Variance Markowitz Model and Using the Different Algorithms
Jamal Bahri Sales Askar Pakmaram Mostafa ValizadehOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not onl MoreOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.Since the objective function of particle swarm algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of this algorithms in the selection of the optimal portfolio. Manuscript profile -
Open Access Article
9 - Using intelligent methods in Solving Constrained Portfolio in Tehran Stock Exchange
Esmat Jamshidi Eyni Hamid KhaloozadehThe optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available asset swhich aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n ri MoreThe optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available asset swhich aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n risky share, how to reach certain profits with minimal risk. Such a portfolio, efficient portfolio is called. For this purpose, the study of evolutionary algorithms, Genetic Algorithm, Imperialist Competitive Algorithm and Particle Swarm Optimization algorithm, also with regard to the basic constraints on the investment, we use these practical methods to solve the portfolio optimization problem. Practical results for the portfolio optimization problem in the Tehran Stock Exchange, of the30 company' sactivein the industry with the selection of20companies along with their validation, is obtained. Aims to help investors better and more practical to select different stocks and thus is an effective investment. Manuscript profile -
Open Access Article
10 - Using intelligent methods in Solving Constrained Portfolio in Tehran Stock Exchange
Esmat Jamshdi Eyni Hamid KhaloozadehThe optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available assets which aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n ri MoreThe optimal portfolio selection problem to find an optimal way to allocate a fixed amount of capital to a set of available assets which aims to maximize expected returns and minimize risk at the same time, to take place. In this Study is shown that an investor with n risky share, how to reach certain profits with minimal risk. Such a portfolio, efficient portfolio is called. For this purpose, the study of evolutionary algorithms, Genetic Algorithm, Imperialist Competitive Algorithm and Particle Swarm Optimization algorithm, also with regard to the basic constraints on the investment, we use these practical methods to solve the portfolio optimization problem. Practical results for the portfolio optimization problem in the Tehran Stock Exchange, of the30 company's active in the industry with the selection of20companies along with their validation, is obtained. Aims to help investors better and more practical to select different stocks and thus is an effective investment. Manuscript profile -
Open Access Article
11 - Predicting negative stock price shocks based on the Meta heuristic approach
Ebrahim fadaei iman dadashi Mohammad javad zare bahnamiri kaveh azinfarAccording to capital market research, the negative shock of stock price in any market is a function of environmental factors and specific characteristics of the company and any insight into how to describe and predict the shock can influence the decisions of investors a MoreAccording to capital market research, the negative shock of stock price in any market is a function of environmental factors and specific characteristics of the company and any insight into how to describe and predict the shock can influence the decisions of investors and stakeholders. In this study, based on the data related to 96 financial ratios of 140 companies listed on the Tehran Stock Exchange during a period of 9 years between 2010 and 3012, we have predicted a negative shock of stock price based on the meta-heuristic approach. In this research, in order to extract the optimal financial ratios, genetic algorithms and particle swarm optimization have been used. The proposed model is then tested using these extracted features by a support vector machine with a radial core and an artificial neural network. The results showed that the variables extracted from the particle swarm optimization algorithm, together with the support vector machine learning algorithm, create better results for predicting shocks (temporary and permanent) and their number. Manuscript profile -
Open Access Article
12 - The Prediction of Iran's Per Capita Health Expenditures up to 2041 Horizon Using the Genetic and Particle Swarm Optimization Algorithms
abolghasem golkhandan Somayeh SahraeiIntroduction: prediction the per capita health expenditures can be useful and effective in determining the best policies for financing and managing of health expenditures. Accordingly, the main objective of this study was to predict the per capita health expenditures tr MoreIntroduction: prediction the per capita health expenditures can be useful and effective in determining the best policies for financing and managing of health expenditures. Accordingly, the main objective of this study was to predict the per capita health expenditures trend in Iran. Methods: In this paper, we specified a health expenditure model relying on theoretical basics in order to obtain desirable forecasts. On the basis of three forms of linear, exponential and quadratic equations and using theoretical foundations in the field of per capita health expenditure function, we used genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to simulate Iranians per capita health expenditure during 1979-2015. Then we selected the superior model in terms of prediction power criteria and forecast per capita health expenditure until 2041. Also, the statistical analyzes were performed using the MATLAB software version R2016b. Results: The predicted results indicate that per capita health expenditures in Iran will increase with a positive slope by 2041. The amount of this expenditure will be from $ 1081 (based on 2011 constant prices) in 2015 to $ 2628 in 2041 (about 2.5 times). Conclusion: With regard to the projected amount of per capita health expenditures up to 2041 horizon, policy makers in the health sector should take the necessary measures to finance the expenditures of this sector. Manuscript profile -
Open Access Article
13 - Presenting a multi-objective model for locating warehouses through particle swarm optimization algorithm in Artaville Tire
Hojatolah Derakhshan Hasan Mehrmanesh Arefe FadaviThis research has been written with the aim of presenting a multi-objective model for locating warehouses through the particle swarm optimization algorithm in Artaville Tire Company. The present study is applied in terms of purpose and in terms of the nature of research MoreThis research has been written with the aim of presenting a multi-objective model for locating warehouses through the particle swarm optimization algorithm in Artaville Tire Company. The present study is applied in terms of purpose and in terms of the nature of research and data collection is a survey and descriptive branch. Data collection tools are documents, documents and interviews with experts. Also, considering that the research seeks to locate the warehouse using the particle swarm optimization algorithm, the research is of a predictive type. Given that this problem falls into the category of Hard-PN problems, a supra-innovative method based on the particle swarm optimization algorithm is used to solve it. To evaluate the performance of the proposed algorithm, two particle group optimization algorithms and genetics have been used as benchmark algorithms. The proposed algorithms and particle group optimization are implemented in 7.5 Matlab programming environment and the genetic algorithm is implemented using Matlab 7.5 software toolbox. According to the results of this study, it was found that the use of particle swarm algorithm to solve the problem of vehicle routing can improve the amount of objective function as well as the total number of routes traveled by vehicles. Manuscript profile -
Open Access Article
14 - Location of well drilling using PSO and AHP algorithms ( Case Study: Mashhad plain)
masuod abdi hossein ebrahimi abolfazl akbarpurDetermining the optimal number of required wells and pumping flow and selecting the place of drilling wells to meet the water needs of different uses is one of the important issues of water resources management in arid and semi-arid regions.The results showed that chang MoreDetermining the optimal number of required wells and pumping flow and selecting the place of drilling wells to meet the water needs of different uses is one of the important issues of water resources management in arid and semi-arid regions.The results showed that changing the coordinates of wells due to the variable thickness of the aquifer and also different water quality in different areas, will affect the cost of extraction and water treatment, respectively, and on the other hand, the distance from the place of consumption will increase the cost of transmission Changing the pumping flow of wells will also affect the energy consumption of the pump in the process of water extraction and transfer. Based on the results, the cost of the optimal design has been reduced by about 10% compared to the existing design. The results showed that water level drop and groundwater quality had the highest weight coefficient.Also, the areas that had the most drop, the best water quality, the shortest distance and the lowest height to the place of consumption, had the highest weight coefficient and the areas that had the lowest drop, The average annual drop in water level in this area varies from 1.13 to 0.3 meters. There are 422 deep wells in this area, about 60 of which have a discharge of more than 20 liters per second and are a good option for replacing the city's drinking water supply wells. Manuscript profile -
Open Access Article
15 - Modeling The Behavior of Concrete Dams using Artificial Neural Network and Logistic Regression Methods
Fardin Saeid Mohsen Irandoust Navid JalalkamaliBackground and Aim: Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the ne MoreBackground and Aim: Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the necessary policies. This study aims to propose a hybrid method involving logistic regression with particle swarm optimization algorithm with real value to predict the behavior of dam equipment.Method: In this study, from 365 days data, from 04/20/2018 to 04/20/2019, of which 600 sets of dam equipment data including parameters of water temperature, water level, valve pressure, sedimentation rate, pore pressure, air temperature, inlet water volume, specific dam characteristics, concrete conditions, reservoir water level, horizontal and vertical displacement, transmission connection components and ground acceleration, strength, pressure, tensile and high stress were used for modeling. Real value-logistic regression and 120 datasets were used for modeling the should be added of particle group optimization algorithm. To evaluate the performance of the proposed method, four statistics including coefficient of determination (R2), root mean square error (RMSE), scattering coefficient (SI), and means bias error (MBE) were used.Findings: The results showed that the model has an acceptable performance in predicting piezometric pressure in the dam body. Also, the results of the artificial neural network model show acceptable convergence with R2 = 0.930 and SSI = 8.587. The results related to the training data of the model also indicate that the mean (µ) and standard deviation (σ) of the proposed model are equal to 1.341 and 1.526 for the training data and these values for the validation data are equal to 1.576 and 2.247, respectively indicating the good performance of the proposed model. In the cumulative probability criterion, the proposed model with P50 = 0.940 and P90 = 1.742 indicates that the results are acceptable.Results: The results indicate that the real value-logistic regression particle swarm optimization implements the principle of structural risk reduction instead of minimizing the experimental risk that provides excellent generalization for small sample sizes. The ratio of predicted piezometric values to read values for about 72% of the data in this model is about one, indicating the appropriate training and predictive power of this model. Finally, according to the evaluation criteria, the hybrid model performs better than the presented methods. Manuscript profile -
Open Access Article
16 - Prediction of Stream Flow Using Intelligent Hybrid Models in Monthly Scale (Case study: Zarrin roud River)
Babak Mohammadi Roozbeh MoazenzadehBackground and Objective: Selecting appropriate inputs for intelligent models are important because it reduces the cost and saves time and increases accuracy and efficiency of its models. The aim of the present study is the use of Shannon entropy to select the optimum c MoreBackground and Objective: Selecting appropriate inputs for intelligent models are important because it reduces the cost and saves time and increases accuracy and efficiency of its models. The aim of the present study is the use of Shannon entropy to select the optimum combination of input variables in the simulation of monthly flow by meteorological parameters. Method: In this study, meteorological data and monthly time series of discharge of Zarrinrood River (Safavankeh Station) in East Azarbaijan from 1336 to 2015 were used. The meteorological parameters and the month of the year were considered as inputs in the entropy method to determine the effective composition. Results: Shannon entropy results showed that the rainfall parameters, month of year and temperature provide better results for modeling. The simulations were performed using intelligent hybrid models of particle swarm hybrid algorithm and hybrid simulation hybrid algorithm. Discussion and Conclusion: The results showed that among these models with the same input structure, the hybrid algorithm simulation based on the support vector machine had better performance for simulating the flow rate compared to other intelligent hybrid models. The results also show that the entropy method is good for selecting the best input combination in smart models. Manuscript profile -
Open Access Article
17 - Optimization and Prediction Changes of Groundwater Quality Parameters Using ANN+PSO and ANN+P-PSO Models (Case Study: Dezful Plain)
Fahimeh Sayadi Shahraki Abdolrahim hooshmand Atefeh Sayadi ShahrakiBackground and Objective: One of the main aims of water resource planners and managers is the estimation and prediction of groundwater quality parameters to make managerial decisions. In this regard, many models have been developed which proposed better managements in o MoreBackground and Objective: One of the main aims of water resource planners and managers is the estimation and prediction of groundwater quality parameters to make managerial decisions. In this regard, many models have been developed which proposed better managements in order to maintain water quality. Most of these models require input parameters which are hardly available or their measurements are time consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by human's brain are a better choice.Method: The present study stimulated the groundwater quality parameters of Dezful plain including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Total Dissolved Solids (TDS), using ANN+PSO and ANN+P-PSO models and in the end is comparing their results with measured data. The input data for TDS quality parameter consist of EC, SAR, pH, SO4, Ca, Mg and Na, for SAR including the TDS, pH, Na, Hco3 and quality parameter of EC contains So4, Ca, Mg, SAR and pH, gathered from 2011 to 2015.Findings: The results indicated that the highest prediction accuracy of quality parameters of SAR, EC and TDS is related to the ANN+P-PSO model so that the MAE and RMSE statistics have the minimum and has the maximum value for the model. The results showed that RMSE for PSO in predicting SAR, EC and TDS were 0.09, 0.045 (µs/cm) and 0.053 (mg/l) in testing period, respectively. These statistical criteria were 0.039, 0.031 (µs/cm) and 0.045 (mg/l) for P-PSO in this period, respectively.Discussion and Conclusion: The results showed that P-PSO had more accuracy compared to PSO. In addition, there were no significant differences between ANN and collecting values. So, it is recommended that ANN were applied to determine nitrate concentration in groundwater. Manuscript profile -
Open Access Article
18 - Application of Genetic Algorithm, Particle Swarm and Artificial Neural Networks in Predicting Profit Manipulation
Morteza Hoseinalinezhad Seyed Mohamad Hassan Hashemi Kucheksarai Ali JafariProfit management has been one of the most controversial topics in recent research. Most research on earnings management has examined the linear relationship between independent variables and earnings management using statistical methods but they did not use these varia MoreProfit management has been one of the most controversial topics in recent research. Most research on earnings management has examined the linear relationship between independent variables and earnings management using statistical methods but they did not use these variables to predict earnings management. Today, with the growth of information technology and the introduction of artificial intelligence, including artificial neural networks into the field of scientific research, it has become possible to study nonlinear relationships between variables. In this study, an attempt was made to estimate optional accruals for predicting earnings management using artificial neural networks. Also in this research, the genetic algorithm optimizer model and Particle swarm has been used to optimize the weights of the artificial neural network model to enhance the predictive power. Then, the ability to predict profit management was evaluated using the modified Jones statistical model, artificial neural network and the combined technique of genetic algorithm, Particle swarm and neural network. The sample used in this study included 150 companies listed on the Tehran Stock Exchange between 2015 and 2020. Findings showed that the artificial neural network has a high ability to predict profit management, compared to the modified Jones linear model. The findings also indicate that the accuracy and ability of the combined model of genetic algorithm, particle swarm and neural network in predicting profit management is higher than the combined model of genetic algorithm-artificial neural network. Manuscript profile -
Open Access Article
19 - usefulness of meta-heuristic algorithms on optimizing of the integrated risk in banking system
eskandar vaziri Farhad dehdar Mohamad reza abdoliaim of this study was to evaluate the integrated risk of the banking system through the metaphysical algorithms of gray wolf, genetics and particle swarming. This research is applied research in terms of purpose and correlational in nature and method. Data collection ha Moreaim of this study was to evaluate the integrated risk of the banking system through the metaphysical algorithms of gray wolf, genetics and particle swarming. This research is applied research in terms of purpose and correlational in nature and method. Data collection has been done through library studies, articles and sites in deductive form and data collection to refute and confirm hypotheses inductively. The statistical population of this research is the banking system and the sample includes banks listed on the Tehran Stock Exchange during the fiscal years 1392 to 1397. In order to collect the required data, the financial database of the Ministry of Economic Affairs and Finance, codal site, etc. have been used. After extracting the information, and setting them in the form of an integrated risk model, the objective function and constraints are entered in MATLAB software and the variables of risk and return (profit and loss on assets and Debts) were obtained using particle swarm algorithms, genetics and gray wolves and we compared their results using SPSS 16 software. After that, first the descriptive statistics were analyzed and then inferential statistics were performed. after reviewing the results of comparing the evaluation indicators of algorithms, it was determined that the gray wolf algorithm is efficient. Provides better goal function optimization. Also, by examining the research hypotheses, it was found that particle swarm algorithms and genetics have the same efficiency for assessing the integrated risk of the banking system. Provides better problem solving. Manuscript profile -
Open Access Article
20 - Relationship between risk and risk - aversion utility Based on Multi-Period prospect theory
RAZIEH Ahmadi Adel Azar gholam reza zomorodianSThe purpose of this study is to investigate the effect of loss-aversion behavior on multi-period investment decisions. For this purpose, two models of portfolio optimization have been designed. Instead of a single-period portfolio model, a three-period model has been us MoreThe purpose of this study is to investigate the effect of loss-aversion behavior on multi-period investment decisions. For this purpose, two models of portfolio optimization have been designed. Instead of a single-period portfolio model, a three-period model has been used. In order to bring the optimization models closer to the real world, in addition to the CVaR as one of the main constraints, the transaction cost and the lower bound and upper bound investment in each asset are also considered. two models of loss aversion and mean-CVaR optimization were solved using PSO algorithm. Also, some important criteria such as initial loss aversion coefficient and reference point are used to test the robustness of model. The results based on the optimal wealth and Sharp ratio showed that loss-averse investors tend to concentrate most of their wealth and have a better performance than rational investors. The impact of CVaR on investment performance was identified. When the market is falling, investors with higher risk aversion avoid extreme losses and obtain more gains. Manuscript profile -
Open Access Article
21 - Presenting the developed model of Benish by using tunneling phenomena based on artificial neural network technique and particle swarm optimization algorithm to identifying profit manipulating companies
Farhad Azadi Mehrdad GhanbarI Babak Jamshidi navid Javad MasodiToday, profit rates and the possibility of managing and manipulating the profits are clear to all, and researchers have always sought solutions to remove the uncertainties facing investors and stakeholders when making their financial decisions. To clarify users' decisio MoreToday, profit rates and the possibility of managing and manipulating the profits are clear to all, and researchers have always sought solutions to remove the uncertainties facing investors and stakeholders when making their financial decisions. To clarify users' decision path of financial data users, Beneish (1999) has developed a profit-management predicting model that has yielded different results in different societies. Thus, this article aims to optimize and localize Beneish’s model by adding the Tunneling variable to Beneish’s variable and using a modern neural network and particle swarm algorithms. The statistical research population consisted of 196 companies listed at the Tehran Stocks Exchange from 2014 to 2019. The research method was a descriptive-library method in which the variables are interrelated through the causal-correlational method. From an objective point of view, it is an Ex-Post Facto research design. To analyze the data, the regression method and artificial neural and the PSO algorithms were used. The model analysis results suggested that all financial ratios had significant effects on Beneish’s profit management, as the Tunneling phenomenon and the financial leverage had the highest and lowest effects on predicting Beneish’s profit management, respectively. Manuscript profile -
Open Access Article
22 - Provide a Earnings Management forecasting model using ant colony and particle swarm algorithm algorithms
Vahid Yousefi HAMIDREZA KORDLOUIE faegh ahmadi mohammadhamed Khanmohammadi Dashti NaderThis study aims to use two ant colony algorithm and particle swarm algorithm to predict earning management and determine which algorithm has more explanatory power.To achieve the research goal, 163 companies have been selected by systematic elimination method in the per MoreThis study aims to use two ant colony algorithm and particle swarm algorithm to predict earning management and determine which algorithm has more explanatory power.To achieve the research goal, 163 companies have been selected by systematic elimination method in the period 2013-2019. The data are panel and thirteen variables have been considered to examine the models. Finally, eight variables have been identified as effective and tests have been performed using Python software. The results show that earnings management can be predicted with more than 97% accuracy by both algorithms, but the ability to predict the particle swarm model in accrual earnings management is higher, however ant colony algorithm has more power in predicting real earnings management. Manuscript profile -
Open Access Article
23 - Earnings Per Share Forecast: the Combination of Artificial Neural Networks and Particle Swarm Optimization Algorithm
Dariush Forougi Heidar Foroughnejad Manochehr MirzaeiExpectations about earning have significant effects on managers and investors’ decisions. Today, one of the measures that are takenin to consideration as an indicator ofcompanies’profitability is the concept of earningpershare.Also earningper share has major MoreExpectations about earning have significant effects on managers and investors’ decisions. Today, one of the measures that are takenin to consideration as an indicator ofcompanies’profitability is the concept of earningpershare.Also earningper share has major effectson stock price of companies. Hence, forecastingearning per shareisof great importance forbothinvestorsandmanagers. The aimof thisstudy is to modelearning pershareforecast of listed companies in Tehran Stock Exchange(TSE) by using the combination ofartificial neural networksand particle swarm optimizationalgorithmbased onunivariate andmultivariate models. To do this,the data of114 companies among the existing listed onesinTehran Stock Exchange was usedduring1380-1389(2001-2010).The results showed that univariate model with 78.5% accuracy and multivariate models with 91.7% accuracy, forecast earning per share. Manuscript profile -
Open Access Article
24 - A cultural algorithm for data clustering
M. R. Shahriari -
Open Access Article
25 - New Artificial Intelligence Modeling for the Photocatalytic Removal of C.I. Acid Yellow 23 in Wastwater
F. Ganbary -
Open Access Article
26 - an approach to detect DDoS attacks in the cloud computing environment using entropy and particle swarm optimization
mehdi asayeshjoo Mehdi sadeghzadeh maziyar ganjooCloud computing is an emerging technology that is widely used to provide computing, data storage services and other remote resources over the Internet. Availability of cloud services is one of the most important concerns of cloud service providers. While cloud services MoreCloud computing is an emerging technology that is widely used to provide computing, data storage services and other remote resources over the Internet. Availability of cloud services is one of the most important concerns of cloud service providers. While cloud services are mainly transmitted over the Internet, they are prone to various attacks that may lead to the leakage of sensitive information. Distributed DDoS attack is known as one of the most important security threats to the cloud computing environment. This attack is an explicit attempt by an attacker to block or deny access to shared services or resources in a cloud environment. This paper discusses a hybrid approach to dealing with DDoS attack in the cloud computing environment. This method highlights the importance of effective feature-based selection methods and classification models. Here, an entropy-based approach and particle swarm optimization to counter these attacks in a cloud computing environment is presented. Classification on high-dimensional data typically requires feature selection as a pre-processing step to reduce the dimensionality. However, effective features selecting is a challenging task, which in this paper uses particle swarm optimization. Here, the proposed classification model is developed based on the use of a balanced binary search tree and dictionary data structure. The simulation is based on the NSL-KDD and CICDDoS2019 datasets, which prove the superiority of the proposed method with an average detection accuracy of 99.84% over the AGA and E-SVM algorithms. Manuscript profile -
Open Access Article
27 - Robust Control of Robot Manipulators using Particle Swarm Optimization Method
Fazlollah Rajaee Seyed Mohammad-Ali Riazi Siamak AzargoshasbIn this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking err MoreIn this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values of the parameters in the torque controllers have not converged to their true values due to the large modelless dynamics, while they have converged to their true values in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed. Manuscript profile -
Open Access Article
28 - Optimal Robust Design of Sliding-mode Control Based on Multi-Objective Particle Swarm Optimization for Chaotic Uncertain Problems
Mohammadjavad Mahmoodabadi Milad Taherkhorsandi -
Open Access Article
29 - Design of an Intelligent Adaptive Control with Optimization System to Produce Parts with Uniform Surface Roughness in Finish Hard Turning
vahid pourmostaghimi Mohammad Zadshakoyan -
Open Access Article
30 - The Inverse Method of Damage Detection using Swarm Life Cycle Algorithm (SLCA) via Modal Parameters in Beam Like Structures
Alireza Arghavan Ali Ghoddosian Ehsan Jamshidi -
Open Access Article
31 - Optimal Routing of Rocket Motion using Genetic Algorithm and Particle Swarm Optimization
Reza Tarighi M.H. Kazemi mohammad hosein khalesi -
Open Access Article
32 - A Trust-based Recommender System Using an Improved Particle Swarm Optimization Algorithm
Sajad Ahmadian Mohammad Hossein OlyaeeIntroduction: Recommender systems are intelligent tools to help users find their desired information among a large number of choices based on their previous preferences in a way faster than search engines. One of the main challenges in recommender systems is the sparsit MoreIntroduction: Recommender systems are intelligent tools to help users find their desired information among a large number of choices based on their previous preferences in a way faster than search engines. One of the main challenges in recommender systems is the sparsity of the user-item rating matrix. This means that users mainly tend to express their opinions about a few items, leading to a large portion of the user-item rating matrix being empty. Trust-based recommender systems aim to alleviate the sparsity problem using trust relationships between users. Trust relationships can be used to calculate similarity values between users and determine the nearest neighbors set for the target user. However, the efficiency of trust-based recommender systems depends on the correct selection of neighboring users for the target user based on the similarity values between users. Method: In this paper, a novel trust-based recommender system is proposed based on an improved particle swarm optimization algorithm. To this end, first, the similarity values between users are calculated based on the user-item rating matrix and trust relationships. Then, the improved particle swarm optimization algorithm is used to optimally weight the neighboring users of the target user. The main purpose of this algorithm is to assign an optimal weight to each user in the nearest neighbor set of the target user to predict the unknown items accurately. After the optimal weighting of neighboring users, unknown ratings are predicted for the target user. Results: The proposed method is evaluated on a standard dataset in terms of mean absolute error, root mean square error, and rate coverage metrics. Experimental results demonstrate the high efficiency of the proposed method compared to other methods. Discussion: We use the genetic algorithms operators and chaos-based asexual reproduction optimization algorithm to improve the original version of the particle swarm optimization algorithm. The genetic algorithms operators increase the exploration mechanism of the particle swarm optimization algorithm, leading to a decline in the probability of tapping into local optima. Moreover, the chaos-based asexual reproduction optimization algorithm is applied to the best solution to further search the area around the best solution. Manuscript profile -
Open Access Article
33 - Improving the Stability of a Power System Including SVC Based on Energy Function Minimization in a Multi-Model Optimal Coordinated Control Structure
Elaheh Pagard Shahrokh Shojaeian Mohammad Mahdi RezaeiIn this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controlle MoreIn this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero. Manuscript profile -
Open Access Article
34 - Optimizing the Control of DFIG Based Wind Turbines Using Sensitivity Analysis and Particle Swarm Optimization Method
Meysam Jaberolansar Mohammad Mahdi Rezaei Hamed Khodadadi Seyed Mohammad MadaniOne of the key issues in the optimal operation of DFIG-based wind turbines is the optimization of relatively large control parameters that exist in these systems. However, the main problem is the high number of control parameters and the nonlinearity of the model of the MoreOne of the key issues in the optimal operation of DFIG-based wind turbines is the optimization of relatively large control parameters that exist in these systems. However, the main problem is the high number of control parameters and the nonlinearity of the model of these systems, which makes solving the optimization problem very time-consuming and divergent in some cases. In this article, in order to optimize the control parameters, a method based on particle swarm optimization (PSO) is proposed. In this method, after linearization of the system model, the eigenvalues of the system are extracted as a function of the control parameters. By examining the sensitivity of eigenvalues to control parameters, more sensitive parameters are identified and optimized based on the PSO method. The performance of the proposed method has been investigated through simulation in the MATLAB software environment. Manuscript profile -
Open Access Article
35 - Optimal Locations on Timoshenko Beam with PZT S/A for Suppressing 2Dof Vibration Based on LQR-MOPSO
M Hasanlu A Bagheri -
Open Access Article
36 - Investigating the Effect of Stiffness/Thickness Ratio on the Optimal Location of Piezoelectric Actuators Through PSO Algorithm
S Jafari Fesharaki S.Gh Madani S Golabi -
Open Access Article
37 - Stable Feature Selection and Clustering According to Hierarchical Structures Based on Chaotic Multispecies Particle Swarm Optimization Applied for Genetic Data Diagnosis and Prognosis
Maryam yassi Mohammad Hossein Moattarb Mehdi YaghoobiAny abnormal reproduction of cells is a tumor. censer happens when there’s an unstrained growth of abnormal cells. Cancer and tumors are divided in to two types, malignant and benign. Given the growth in the environmental information, it’s essential to emplo MoreAny abnormal reproduction of cells is a tumor. censer happens when there’s an unstrained growth of abnormal cells. Cancer and tumors are divided in to two types, malignant and benign. Given the growth in the environmental information, it’s essential to employ some tools to analyze this data and gain the knowledge embedded in it. Since large-scale problems and huge data bases are incomprehensible for the human, employing intelligent methods is effective in understanding large-scale data better. In this paper, the integration methods are a subset of rating measures each with a specific objective of sustainable features for superior selection of distinct features.The next step would discuss creating a fuzzy system (FS) to detect and classify between benign and malignant nature of biological data. Fuzzy system type is Takagi-Sugeno-Kang (TSK). To classify a hierarchical structure of multi-species particle swarm algorithm based on chaotic particle can be used to optimize the fuzzy system. In addition, using chaotic theory discerns the true diversity of the particles and increases the power to detect and classify the samples. Accurate identification and classification of malignant and benign biological nature of the data is more than 95%. This simulation is performed on UCI and Microarray data-base. Manuscript profile -
Open Access Article
38 - Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization (pso)
Aliasghar Davoodi Kasbi Iman Dadashi -
Open Access Article
39 - Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)
Bahman Talebi Rasoul Abdi Zohreh Hajiha Nader Rezaei -
Open Access Article
40 - A new two-phase approach to the portfolio optimization problem based on the prediction of stock price trends
Hamid Reza Yousefzade Amin Karrabi Aghileh Heydari -
Open Access Article
41 - Agility Agents In Supply Chain of Educational Organizations Using Particle Swarm Optimization Algorithm
Abbass Toloie Ashlaghi shahrzad tayaran Reza Radfar Alireza PourebrahimiThe increasing speed of technological change, on the one hand, and the changing nature of customer demand and the intensification of competition among organizations, on the other hand, have led organizations to seek to take on new competitive advantages to outperform co MoreThe increasing speed of technological change, on the one hand, and the changing nature of customer demand and the intensification of competition among organizations, on the other hand, have led organizations to seek to take on new competitive advantages to outperform competitors and better meet customer needs. Achieving such goals comes in the context of a new concept called "organizational agility," but agility of the organization is influenced by its agents, which are the most influential factor in service companies. In this research, which the University of Science and Research has proposed as a case study, the employees are divided into three categories: Soft, Grievous, and Blind. These factors determine the three main elements of the agility of the supply chain organization: Agility drivers, agility abilities and agility capability. Also, using a particle swarm optimization algorithm, an intelligent model has been designed to measure the impact and impact of factors on each other. And after implementing the model in a case study at Time = 769, recovery is at best possible. Manuscript profile -
Open Access Article
42 - Solving Resource-Constrained Project Scheduling Problem with Particle Swarm Optimization (Case Study: Bandar Abbas Gas Condensate Refinery)
Mohammadhusein Nabizadeh Huseinali Hasanpoor Roozbeh Azizmohammadi Navid HashtroodiOne of the issues considered by the projects responsible especially project managers is the execution of project activities according to time schedule. The very difficult nature of that issue is also another reason for the researchers to take much note of it. Therefore, MoreOne of the issues considered by the projects responsible especially project managers is the execution of project activities according to time schedule. The very difficult nature of that issue is also another reason for the researchers to take much note of it. Therefore, there are especial techniques and methods to solve those issues. Also, project managers pay much attention to the stability of the time schedule as it is important for them. This paper is provided with a real project time schedule for a refinery by using stable time schedule. Particle swarm optimization algorithm is suggested to resolve the problem since the project time schedule has resources limitation including NP- Hard. In order to accesses the validation of the model, 4 issues with small scales has been selected and the results from the suggested algorithm was compared with the accurate result obtained from lingo software. These results indicate that the suggested algorithm is effective and convergent with the optimized result. Manuscript profile -
Open Access Article
43 - Optimizing the cost of asphalt road pavement using particle swarm optimization algorithm (PSO) and compare it with the Shell method
Mansour Tohidi Navid Khayat Abdoulrasoul Telvari -
Open Access Article
44 - Intelligent Identification of Centrifugal Pump Damage by Combining Methods Independent Component Analysis and Particle Swarm Optimization
Mohammad sadegh Aalaei Mehdi ShekarzadehDue to the progress of technical and engineering sciences and the more complex equipment and machinery in recent years, the maintenance and repair technology based on condition monitoring and defect estimation, under different titles such as performance-based logistic ( MoreDue to the progress of technical and engineering sciences and the more complex equipment and machinery in recent years, the maintenance and repair technology based on condition monitoring and defect estimation, under different titles such as performance-based logistic (PBL) and condition-based maintenance (CBM) is considered. These methods are used to prevent human and financial losses and to increase the production rate. This thesis presents an intelligent troubleshooting system to diagnose centrifugal pump-bearing faults. As a result, to design this intelligent troubleshooting system, a test set including shaft, bearings and real support conditions was designed and implemented in the laboratory. In this setup, three bearings with Normal wear and fault conditions (defect on the outer race) were examined, and vibration data were obtained. Then, the vibration data in extraction time and statistical features were calculated. After that, these features were used as classifier input data for intelligent troubleshooting. To identify the defect, the independent component analysis method was used. Also, the accuracy of fault detection was improved by using the particle batch optimization method. Finally, it was found that the statistical feature of Percentile can detect bearing defects by combining independent component analysis and particle swarm optimization methods. Manuscript profile -
Open Access Article
45 - Particle swarm optimization in optimal control problems for Car on a constrained piecewise affine hill
Ahmad Kia Kojouri Javad MashayekhiFardIn spite of all the Demonstrate Prescient Control (MPC) based arrangement preferences such as ensuring soundness, the most impediment such as an exponential development of the number of the polyhedral locale by expanding the expectation skyline exists. This causes an in MoreIn spite of all the Demonstrate Prescient Control (MPC) based arrangement preferences such as ensuring soundness, the most impediment such as an exponential development of the number of the polyhedral locale by expanding the expectation skyline exists. This causes an increase in the computation complexity of control law. In this paper, we consider the arrangement to ideal control issues for constrained piecewise affine systems based on demonstrating predictive control. After that, we utilize particle swarm optimization calculation to complexity diminishment of arrangement and alter the framework execution. In truth the point of the paper is twofold. To begin with, we consider the hypothetical comes about on the structure of the control law. At the minute, we portray how the complexity of deciding control law can be capable of decreased and moving forward system execution at the same time by utilizing particle swarm optimization. The considered calculation is associated with a Car on a constrained piecewise affine hill and the result is shown to the advantage of our analysis. The objective of the car is to climb to the top of a steep slope and then preserve its position at the top (the beginning), without falling from the piecewise affine environment. The number of control law polyhedrals diminishes from 129 to 25. Manuscript profile -
Open Access Article
46 - Predicting distribution pattern of Bemisia tabaci G. ( (Hem.: Aleyrodidae) by Hybrid neural network With Particle Swarm Optimization Algorithm
Alireza Shabaninejad Bahram TafaghodiniyaToday, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm MoreToday, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm Optimization in surface of cucumber field. Population data of pest was obtained in 2017 by sampling in 100 fixed points in a fallow field in Ramhormoz, to evaluate the ability of neural networks combined with Particle Swarm Optimization to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and coefficient determination of linear regression among predicted values and actual values. Results showed that in training and test phases of neural network combined Particle Swarm Optimization algorithm, was no significant effect between variance, mean and statistical distribution of actual values and predicted values. Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field. Manuscript profile -
Open Access Article
47 - Optimization of two dimensional structures, using minimum growth ground method with floating nodes
Ali Ghoddosian Saber Meskar Jahan abadOptimization techniques based on basic structure and the minimum growth ground method and stationary nodes, are among useful and effective methods in finding optimal discrete structures. But the slow optimization process of the base structure method for two-dimensional MoreOptimization techniques based on basic structure and the minimum growth ground method and stationary nodes, are among useful and effective methods in finding optimal discrete structures. But the slow optimization process of the base structure method for two-dimensional Large-scale structures and the large number of basic structural elements, waste a lot of time to solve problems. The fixed nodes of the truss in minimum growth ground method with fixed nodes, which in some cases makes structures to find a local optimum. In this article we have tried to provide a new method, optimization of cross section and truss topology are in a way that the aforementioned problems are solved as far as possible and for this purpose the algorithm of growth ground method with floating nodes are suggested. In this method, instead of starting with the base structure with the most possible members of the structure, we start with a structure with minimum members and the basic structure grows until it satisfies the conditions and all nodes are added to the structure in a floating mode. Then, in this paper several standard example are used which are coded using MATLAB software and the obtained results in the form of growth ground method with floating nodes using optimization algorithm of particle swarm is compared to other methods. The results show that growth ground method with floating nodes has a very high Convergence rate compared to growth ground method and more Absolute optimization compared to the minimum growth ground method with fixed nodes. Manuscript profile -
Open Access Article
48 - Solving Security Constrained Unit Commitment by Particle Swarm Optimization
Shiva Alipour Ghorbani Hossein Nasiraghdam -
Open Access Article
49 - A PSO-Based Static Synchronous Compensator Controller
Meisam Mahdavi Ali Nazari Vahid Hosseinnezhad Amin Safari -
Open Access Article
50 - Load Frequency Control in Power Systems Using Improved Particle Swarm Optimization Algorithm
Milad Babakhani Qazijahan -
Open Access Article
51 - MMDT: Multi-Objective Memetic Rule Learning from Decision Tree
Bahareh Shaabani Hedieh Sajedi -
Open Access Article
52 - PSO-Based Path Planning Algorithm for Humanoid Robots Considering Safety
Roham Shakiba Mostafa E. Salehi -
Open Access Article
53 - Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW)
Mojtaba Gholamian Mohammad Reza Meybodi -
Open Access Article
54 - Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms
Maryam Ghodsi Mohammad Saniee Abadeh -
Open Access Article
55 - Negative Selection Based Data Classification with Flexible Boundaries
Lena Nemati Mojtaba Shakeri -
Open Access Article
56 - A Comparative Study of Four Evolutionary Algorithms for Economic and Economic-Statistical Designs of MEWMA Control Charts
سید تقی اخوان نیاکی مهدی Malaki محمد جواد ارشادی -
Open Access Article
57 - Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling
Javad Behnamian -
Open Access Article
58 - A Multi-Objective Particle Swarm Optimization for Mixed-Model Assembly Line Balancing with Different Skilled Workers
Parviz Fattahi Parvaneh Samouei -
Open Access Article
59 - Hub Covering Location Problem Considering Queuing and Capacity Constraints
Mehdi Seifbarghy Mojtaba Hemmati Sepideh Soltan Karimi -
Open Access Article
60 - Three Hybrid Metaheuristic Algorithms for Stochastic Flexible Flow Shop Scheduling Problem with Preventive Maintenance and Budget Constraint
Sadigh Raissi Ramtin Rooeinfar Vahid Reza Ghezavati -
Open Access Article
61 - Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II
Fariba Maadanpour Safari Farhad Etebari Adel Pourghader Chobar -
Open Access Article
62 - Designing Stochastic Cell Formation Problem Using Queuing Theory
Parviz fattahi Bahman Esmailnezhad Amir Saman Kheirkhah -
Open Access Article
63 - Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering Problem
esmaeil Mehdizadeh reza Tavakkoli Moghaddam -
Open Access Article
64 - Optimizing the Prediction Model of Stock Price in Pharmaceutical Companies Using Multiple Objective Particle Swarm Optimization Algorithm (MOPSO)
Ali Khazaei Babak Haji Karimi Mohammad Mahdi Mozaffari -
Open Access Article
65 - Joint Pricing and Inventory Routing Modeling in a Two Echelon Closed Supply Chain
mohamad mohamadnejad Isa nakhaei kama abadi Ramin Sadeghian Fardin Ahmadi zarAbstract This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximi MoreAbstract This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximize the profit. In the study, a non-linear mathematical model is presented for small scale problems. Due to the NP-hardness of the problem, two met heuristics, genetic algorithm and particle swarm optimization algorithm, are applied to solve medium and large scale problems. The algorithms are validated by comparing their results with those of the mathematical model. Finally, the performance comparison of the two met heuristics through statistical analysis is demonstrated that the particle swarm optimization algorithm performance outperforms the genetic algorithm. Manuscript profile -
Open Access Article
66 - Solving the Problem of Scheduling Unrelated Parallel Machines with Limited Access to Jobs
Mohammadreza Naghibi Abolfazl Adressi -
Open Access Article
67 - A Heuristic Approach for Optimization of Gearbox Dimension
Mehrdad Hosseiniasl Javad Jafari Fesharaki -
Open Access Article
68 - STATCOM controller design with using of improved robust backstepping algorithm based on PSO to reduce large signal disturbances in power systems
Fariborz Haghighatdar Fesharaki Alireza HaghshenasIn this paper, in order to reduce disturbance attenuation in the single-machine infinite-bus system a STATCOM by an improved robust back-stepping algorithm based on a particle swarm optimization approach is proposed. In the proposed approach, the adaptive back-stepping MoreIn this paper, in order to reduce disturbance attenuation in the single-machine infinite-bus system a STATCOM by an improved robust back-stepping algorithm based on a particle swarm optimization approach is proposed. In the proposed approach, the adaptive back-stepping method is used to construct the storage function to reduce internal and external disturbances. Also, a nonlinear controller with interference rejection feature and update of the nonlinear parameter substitution law are applied simultaneously. In this research, in order to maintain non-linearities feature, the real-time estimation of uncertain parameters, ensure robustness and insensitivity to large disturbances of the STATCOM system, the adaptive back-stepping sliding mode control method is applied in terms of error compensation design. It should be noted that the proposed controller has a large number of design parameters which affect its efficiency and performance. So, here the particle swarm optimization approach is used to determine the design parameters based on the cost function of the integral of the magnitude of the error. Finally, the simulation results are performed by MATLAB software, confirmed the better performance of the proposed optimal back-stepping sliding mode control method compared to traditional adaptive back-stepping in terms of the speed of adaptation and the response of the STATCOM system. Manuscript profile -
Open Access Article
69 - Optimal Design, Modeling, and Evaluation of Single-Phase Axial Flux Induction Motor with a Permanent Capacitor Using Improved Particle Swarm Optimization Algorithm (IPSO)
Amin Aboutalebi NajafabadiThe increasing application of single-phase axial flux induction motors with a permanent capacitor and their low efficiency has led to the importance of optimization of this type of motors. In this paper, by introducing the classical algorithms of design of this type of MoreThe increasing application of single-phase axial flux induction motors with a permanent capacitor and their low efficiency has led to the importance of optimization of this type of motors. In this paper, by introducing the classical algorithms of design of this type of motors, which consists of finding the dimensions of different parts of the motor and calculation of electrical parameters such as resistance and reactance, and capacitor, by introducing the proposed equivalent circuit in the permanent state to reduce the air gap of the motor, introduces the structure of optimization algorithms and then uses a genetic algorithm and improved particle swarm algorithm to optimize the design of the axial flux motor to increase efficiency, increase power factor and reduce core volume. For this purpose, a single-phase axial flux induction motor with a permanent capacitor that has considerable application in ventilation systems is investigated, and using design formulas and with the help of a circuit equivalent to the proposed permanent state, as well as using Intelligent methods such as genetic algorithm and improved particle swarm algorithm, engine optimization to increase maximum efficiency and the results are drawn in the form of torque-speed and efficiency-speed diagrams and compared with each other. Finally, the designed motor is simulated by the finite element method to verify the design algorithm, the steady-state model, the proposed optimization algorithm, and the test results. Manuscript profile -
Open Access Article
70 - Wireless Sensor Networks Routing Using Clustering Based on Multi-Objective Particle Swarm Optimization Algorithm
Seyed Reza Nabavi Nafiseh Osati Eraghi Javad Akbari TorkestaniWith the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks MoreWith the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks are one of the best options for collecting data from the environment due to their easy configuration and no need for expensive equipment. The energy of sensors in wireless sensor networks is limited, which is a major challenge due to the lack of a fixed charge source. Because most of the sensors' energy is wasted during data transmission, a sensor that transmits more data than others and transmits data over long distances with packets will run out of energy sooner than others. When a sensor in the network runs out of energy, the network process may be disrupted. Therefore, due to the dynamic topology and distributed nature of wireless sensor networks, designing energy efficient routing protocols is one of the main challenges. Therefore, in this article, energy-aware routing protocol based on multi-objective particle swarm optimization algorithm is presented. In the proposed approach, the fitness function of the particle swarm optimization algorithm for selecting the optimal cluster head based on quality-of-service goals including residual energy, link quality, end-to-end delay and delivery rate. The simulation results show that the proposed approach has less energy consuming and extend network lifetime due to balancing the goals of quality-of-service criteria than other approaches. Manuscript profile -
Open Access Article
71 - Optimal Design of a Hybrid Solar–Wind–Battery System using the Grasshopper Optimization Algorithm for Minimization of the Loss of Power Supply Probability
Ronak Jahanshahi Bavandpour Hamid Ghadiri Hamed KhodadadiRenewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an afforda MoreRenewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an affordable system based on storing the solar and wind energy. In this paper, optimization of a solar-wind hybrid system is presented with a saving battery system for supplying a specific hourly load annually to minimize annual system expenses and the probability of Loss of Power Supply Probability (LPSP). Annual expenses of the system include initial investment, maintenance, and replacement costs. The purpose of optimization is to determine the numbers of solar panels, wind turbines, batteries, the height of the wind tower, and the angle of the solar panel toward solar radiation. For this issue, a new method named Grasshopper Optimization Algorithm (GOA) is employed. Also, the effects of changes in inverter efficiency, load demand, and of maximum probability of LPSP on system designing are evaluated. Simulation results show that the efficiency reduction, load increase, and increasing the load and maximum reliability in the system in the form of reducing of LPSP lead to an increase in annual energy costs of systems. Furthermore, the results indicate the superiority of the GOA method toward particle swarm optimization (PSO) in reaching better target function and less cost. Manuscript profile -
Open Access Article
72 - Optimizing the Cutting of Inconel 718 Sheets with CO2 Laser by Particle Swarm Algorithm
Saeid Kiani Rasoul Tarkesh Esfahani Zahra ZojajiIn this paper, the impact of different operative variables on the quality of cutting of Inconel material 718 is studied. Utilizing Taguchi test design, the input variables including carbon dioxide laser power and the cutting speed for cutting three different thicknesses MoreIn this paper, the impact of different operative variables on the quality of cutting of Inconel material 718 is studied. Utilizing Taguchi test design, the input variables including carbon dioxide laser power and the cutting speed for cutting three different thicknesses of Inconel 718 alloy were investigated in order to achieve the optimal conditions. After obtaining experimental test results, dataset was modeled using artificial neural networks. The neural network model is then used for evaluating candidate solutions in particle swarm optimization (PSO) algorithm which is employed for optimization of cutting conditions. The results indicated that when the laser power of is 1714 (W), the cutting speed is 1382 (mm/min) and the thickness of the material is 0.8 (mm), The best quality for cutting Inconel 718 is achieved with a carbon dioxide laser cutting machine. The results of optimal cutting parameters of Inconel alloy with carbon dioxide laser which were obtained by PSO were verified through an experimental test and similar papers. The results of this experimental test were very close to the optimal values of the PSO, which demonstrates the efficiency of neural network model in predicting the quality of cutting and the efficiency of PSO in finding optimal conditions. Manuscript profile -
Open Access Article
73 - Increase the Efficiency of the Offloading Algorithm in Fog Computing by Particle Swarm Optimization Algorithm
Seyed Ebrahim Dashti Hoasain ZareEdge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage op MoreEdge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage operations take place close to objects and data sources. In order to reduce latency and network traffic between end devices and cloud centers, groups at the edge have processing capabilities, perform a large number of processing and computing tasks, including data processing, temporary storage, device management, decision making, and privacy protection. Since the number of edge devices is large, there must be a mechanism to select these tasks and offload them to the cloud. The problem to be decided is that which one of the available edge devices should be selected for unloading and then unloaded. This problem is classified as one of the hard non-polynomial problems and by using deterministic algorithms simply and in polynomial time, it is not possible to find a suitable and efficient solution for it found. Manuscript profile -
Open Access Article
74 - Reduction of Sub-synchronous Resonances with D-FACTS Devices using intelligent Control ,
Zahra Amini Abbas KargarWhen a turbine–generator set connect to a long transmission line, may results side effects such as Sub-Synchronous Resonances (SSR). The capabilities of the Distributed Static Series Compensator (DSSC) as a member of the family of D-FACTS can be used to reduce the MoreWhen a turbine–generator set connect to a long transmission line, may results side effects such as Sub-Synchronous Resonances (SSR). The capabilities of the Distributed Static Series Compensator (DSSC) as a member of the family of D-FACTS can be used to reduce these SSR. To achieve this desired goal, the fuzzy controller, Particle Swarm Optimization (PSO) and artificial neural network is used to control of the DSSC. Particle swarm optimization is designed Based on the Conventional Damping Controller (CDC) and fuzzy logic is designed based on damping controller (FLBDC) and damping control based on artificial neural network trained using the fast pace of changes has been designed. Stability of the system is analysed by simulations in the time domain with performance index (PI). All simulations are done using Matlab / Simulink software. Case studies show that proposed algorithms can reduce SSR in the system.All simulations are done using Matlab / Simulink software. Case studies show that proposed algorithms can reduce SSR in the system. Manuscript profile -
Open Access Article
75 - Optimal PID Controller Tuning for Multivariable Aircraft Longitudinal Autopilot Based on Particle Swarm Optimization Algorithm
Mostafa Lotfi Forushani Bahram Karimi Ghazanfar ShahgholianThis paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required MoreThis paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis) based on particle swarm optimization (PSO) algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper, the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller. Manuscript profile -
Open Access Article
76 - Estimation of Behavior Coefficient of steel eccentrically braced frames (EBFs) under Near-fault Pulse-type Earthquakes using Particle Swarm algorithm
Seyed Abdonnabi Razavi Navid Siahpolo Mehdi Mahdavi AdeliElastic analysis of structures creates basic shearing forces and stresses that are significantly larger than the actual responses of the structures. By entering the inelastic domain, the structure can absorb and resist a large amount of earthquake energy. On the other h MoreElastic analysis of structures creates basic shearing forces and stresses that are significantly larger than the actual responses of the structures. By entering the inelastic domain, the structure can absorb and resist a large amount of earthquake energy. On the other hand, nonlinear analysis of structures requires time-consuming and voluminous computational operations, so in most of the codes, a simple and appropriate method called equivalent static method is presented to achieve a reasonable answer to the nonlinear behavior of the structure (without performing a nonlinear analysis). Therefore, due to the importance of ductility in the absorption of seismic energy, the computational forces caused by the earthquake are reduced by introducing the coefficient of structural behavior, R. In this paper, an extensive database consisting of 12,960 eccentrically braced frame (EBF) structures with varying story numbers of 3, 6, 9, 12, 15 and 20, three types of column stiffness and three degrees of bracing slenderness was designed and analyzed under 20 near-faults pulse-like earthquakes. To generate the estimated relation R, 6769 data were interpreted using particle swarm algorithm. The results of a correlation of 0.86 in the test data presented the accuracy of the proposed relation. Manuscript profile -
Open Access Article
77 - Multilayer Paraboloid Structures Optimization of Using a Hybrid Charged System Search
Amir abbaspour siamak Talaat ahariSpace structure is a rigid, lightweight, truss-like structure constructed from interlocking struts in a geometric pattern. Space structure can be covered large areas without intermediate supports.In this paper, the problem of simultaneous shape and size optimization of MoreSpace structure is a rigid, lightweight, truss-like structure constructed from interlocking struts in a geometric pattern. Space structure can be covered large areas without intermediate supports.In this paper, the problem of simultaneous shape and size optimization of a three-layer paraboloid space structure is addressed. In this method, the hybrid charged system search-particle swarm is utilized as the optimization algorithm and the result is compared with the particle swarm optimization algorithm. The objective of this paper is to find optimal weight, that design variables are considered as height and cross-sectional area. For conducting this, a three-layer paraboloid space structure is designed by SAP and then optimized by using hybrid charged system search-particle swarm and particle swarm optimization algorithms. The result demonstrate the efficiency of the hybrid charged system search-particle swarm algorithm. Manuscript profile -
Open Access Article
78 - A Modified Particle Swarm Optimization Algorithm with Dynamic Mutation and its Application to Optimal Design of Reinforced Concrete Moment Frames
siamak alizad ahmad malekiThe main purpose of the current research is to develop a new mechanism that makes PSO to work better. Optimization is a tool to find the best solution of a multi-modal problem. Nowadays engineers and designers are looking for optimal designs due to restriction of resour MoreThe main purpose of the current research is to develop a new mechanism that makes PSO to work better. Optimization is a tool to find the best solution of a multi-modal problem. Nowadays engineers and designers are looking for optimal designs due to restriction of resources. Recently many optimization algorithms have been developed to takle complex problems, such as Particle Swarm Optimization (PSO). This algorithm is known as a strong explorer but weak exploiter. The main problem is many internal parameters to be tuned. So that users should achive a set of sensitivity analyses to adjust them for the problem at hand, but in the mean time the obtainded values may not proper for other problems. In the present work, in order to overcome this shortcoming, a mutation mechanism as an important element of genetic algorithm is implemented in PSO. Each particle in the optimization proccess is assigned with a random number, then, as the procedure goes on, the random number of each individual is compared with a treshhold and if it is smaller than the treshhold, the particle gets mutated. This new algorithm entitled as modified particle swarm optimization (PSO). Three reinforced concrete MRF optimum design problems are solved by MPSO, PSO and results compared together with another research results based on HS algorithm. Studying the results show that MPSO compared with adjusted PSO by sensitivity analysis and HS, not only brings to better solutions, but it also does not need to any manual adjustment by sensitivity analysis. Manuscript profile -
Open Access Article
79 - مدل کنترل موجودی بهینه محصولات منسوخ شدنی با در نظر گیری تاخیر مجاز در پرداخت و تقاضا وابسته به زمان، با استفاده از الگوریتم PSO
حسن زمانی باجگانی محمدرضا غلامیاناین مطالعه یک مدل کنترل موجودی را برای تعیین چرخه بهینه بازپرسازی اقلام منسوخ شدنی ارائه میکند، که در آن تقاضای مشتری به صورت یک تابع کاهشی از زمان در حالت منسوخ شدن ناگهانی در نظر گرفته شده است. علاوه بر این، برای تشویق خریدار به خرید بیشتر، فروشنده می تواند به خریدار Moreاین مطالعه یک مدل کنترل موجودی را برای تعیین چرخه بهینه بازپرسازی اقلام منسوخ شدنی ارائه میکند، که در آن تقاضای مشتری به صورت یک تابع کاهشی از زمان در حالت منسوخ شدن ناگهانی در نظر گرفته شده است. علاوه بر این، برای تشویق خریدار به خرید بیشتر، فروشنده می تواند به خریدار اجازه دهد هزینه را با تاخیر پرداخت کند. بر این اساس، مقاله حاضر بر بررسی یک مدل کنترل موجودی برای اقلام منسوخ شدنی با در نظر گرفتن سیاست اعتبار تجاری و تقاضای وابسته به زمان و حالت منسوخ شدن ناگهانی تمرکز دارد. با توجه به غیر خطی بودن مدل پیشنهادی، از تقریب سری تیلور برای حل آن استفاده شد. علاوه بر این، برای جلوگیری از تأثیر تقریب سری تیلور بر راهحل بهینه، از یک الگوریتم فراابتکاری بهینهسازی ازدحام ذرات کارآمد برای یافتن راهحل نزدیک به بهینه استفاده شد که نشاندهنده پاسخهای بهتر است. سپس مثالهای عددی در مورد مطالعاتی صنعت عمده فروشی تلفن همراه برای نشان دادن اعتبار مدل پیشنهادی در نظر گرفته و حل شد. در نهایت، یک تحلیل حساسیت در زمینه اثرات پارامترهای اصلی بر سود کل و زمان چرخه بازپرسازی انجام شد. نتایج عددی حاکی از آن است که گنجاندن ریسک منسوخ شدگی در مدل موجودی کالا برای اقلام منسوخ شدنی در افزایش سود و در عین حال کاهش هزینه های این اقلام تاثیر بسزایی دارد. Manuscript profile -
Open Access Article
80 - An Improved Algorithmic Method for Software Development Effort Estimation
Elham Khatibi Vahid Khatibi Bardsiri -
Open Access Article
81 - An Improved Particle Swarm Optimization Algorithm for Energy Management in Distribution Grid Considering Distributed Generators
Hossein Lotfi Reza Ghazi Mohammad Bagher Naghibi Sistani -
Open Access Article
82 - Heuristic algorithms for task scheduling in Cloud Computing using Combined Particle Swarm Optimization and Bat Algorithms
Behnam Barzegar Samaneh Habibian Mehrnoush Fazlollah Nejad -
Open Access Article
83 - Task Scheduling Using Particle Swarm Optimization Algorithm with a Selection Guide and a Measure of Uniformity for Computational Grids
Mona Torabi -
Open Access Article
84 - Application of Swarm-Based Optimization Algorithms for Solving Dynamic Economic Load Dispatch Problem
Alireza Khosravi Mohammad Yazdani-Asrami -
Open Access Article
85 - Application of New Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for Non Convex Economic Load Dispatch Problem
Mani Ashouri Seyed Mehdi Hosseini -
Open Access Article
86 - Flexible Beam Robust Loop Shaping Controller Design Using Particle Swarm Optimization
Roja Eini -
Open Access Article
87 - Solving Flexible Job-Shop Scheduling Problem using Hybrid Algorithm Based on Gravitational Search Algorithm and Particle Swarm Optimization
Behnam Barzegar Homayun Motameni -
Open Access Article
88 - Optimal Design of Three Phase Surface Mounted Permanent Magnet Synchronous Motor by Particle Swarm optimization and Bees Algorithm for Minimum Volume and Maximum Torque
Sahra Khazaei Abdolhossein Tahani Mohammad Yazdani-Asrami S. Asghar Gholamian -
Open Access Article
89 - بهینه سازی برنامه ریزی تولید با استفاده از الگوریتم ژنتیک و بهینه سازی ذرات (مطالعه موردی: کارخانه چای صوفی)
منصور صوفی مریم محسنی -
Open Access Article
90 - Effects of Atmospheric Changes on Reducing the Performance of Solar Panels by Particle Swarm Optimization Algorithm
Shahrokh Jalili Elay Mehrpourazari -
Open Access Article
91 - Application of orthogonal array technique and particle swarm optimization approach in surface roughness modification when face milling AISI1045 steel parts
Masoud Azadi Moghaddam Farhad Kolahan -
Open Access Article
92 - A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain
Mehdi Seifbarghy Masoud Mirzaei Kalani Mojtaba Hemmati -
Open Access Article
93 - Solving Fractional Programming Problems based on Swarm Intelligence
Osama Abdel Raouf Ibrahim M. Hezam -
Open Access Article
94 - Bi-product inventory planning in a three-echelon supply chain with backordering, Poisson demand, and limited warehouse space
Maryam Alimardani Fariborz Jolai Hamed Rafiei -
Open Access Article
95 - Multi-period project portfolio selection under risk considerations and stochastic income
Ali Asghar Tofighian Hamid Moezzi Morteza Khakzar Barfuei Mahmood Shafiee -
Open Access Article
96 - An improved particle swarm optimization with a new swap operator for team formation problem
Walaa H. El-Ashmawi Ahmed F . Ali Mohamed A. Tawhid -
Open Access Article
97 - A New Optimal Correlation for Behavior factor of EBFs under Near-fault Earthquakes using Artificial Intelligence Models
Seyed Abdonnabi Razavi Navid Siahpolo -
Open Access Article
98 - Improving Reliability by Optimal Allocation of Protection Devices and Distributed Generation Units
حمیدرضا اکبری Amirhosein Bolurian Mahmoud Modaresi -
Open Access Article
99 - Integrating Wind Farms and Pumped Storage Plants in Power System Unit Commitment Using Modified Particle Swarm Optimization
Hassan Siahkali -
Open Access Article
100 - Customer Clustering by Combining the Particle Swarm And K-Means Algorithms and Analyzing Their Behavior on Commercial Websites
MohammadReza Mehrazma Behrad Mahboobi -
Open Access Article
101 - Joint Coordination of Wind Farms and Pumped Storage Plants in Generation Scheduling Using Modified Particle Swarm Optimization with Bacteria Foraging Concept
Hassan Siahkali -
Open Access Article
102 - Examining the Efficiency Models, Genetic Algorithm under MSV Risk and Particle Swarm Optimization Algorithm under CVAR Risk Criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
Dariush Adinevand Ebrahim Ali Razini Mahmoud Khodam Fereydoun Ohadi Elham Elsadat HashemizadehAbstract Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purp MoreAbstract Choosing the optimal stock portfolio is one of the main goals of capital management. Today, There are several tools and techniques for measuring portfolio risk and selecting the optimal stock portfolio. In this article, using data of 15 shares selected by purposeful sampling method from the top companies of Tehran Stock Exchange Organization including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days and then using MATLAB software models The Metaheuristic Optimization of the Genetic Algorithm under the MSV Risk Criterion and the Particle Swarm Algorithm under the CVaR risk Criterion are Compared. The results show that the genetic algorithm model under MSV risk criterion is more efficient and less risky, therefore the genetic algorithm model under MSV risk criterion is more efficient than the particle swarm algorithm model under CVaR risk criterion. Manuscript profile -
Open Access Article
103 - Predicting Negative Price Shock with Emphasis on Financial Ratios
Ebrahim Fadaii Mohammad Javad ZareBahnamiriAbstractAccording to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors MoreAbstractAccording to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors and activists in the stock market. In this study, based on data related to 140 companies listed on the Tehran Stock Exchange.we have attempted to predict stock price shocks with emphasis on financial ratios. In order to select the optimal variables from the set of 96 variables, two evolutionary algorithms of particle swarm optimization and genetic algorithm have been used. After applying the mentioned algorithms, finally, 8 variables affecting permanent and temporary shocks were extracted, which in the regression model mentioned in the research, their effect on the predictor of shock was investigated. the results of RSME model are the permanent shock (genetic algorithm), permanent shock (particle swarm optimization), temporary shock (genetic algorithm) and temporary shock (particle swarm optimization (particle swarm optimization), 5.8433 , 5.6284 , 7.537 and 7.295 . as we observe , RSME in permanent shock based on genetic algorithm is more than RSME permanent shock model based on the evolutionary algorithm of particle swarm optimization. also in the transient shock model based on the genetic algorithm , the model is more than RSME of the temporary shock model based on the evolutionary algorithm of particle swarm optimization . It can therefore be stated that the estimated regression is based on the selected variables from the evolutionary algorithm of the particle swarm optimization, and has better predictive power than the selected variables of the genetic algorithm. Manuscript profile -
Open Access Article
104 - Optimal Detection of Oil Contamination at Sea by the FPSO Algorithm
mostafa zamani mohiabadi -
Open Access Article
105 - Simultaneous Placement of Capacitor and DG in Distribution Networks Using Particle Swarm Optimization Algorithm
Hossein Lotfi Mohammad Borhan Elmi Sina Saghravanian -
Open Access Article
106 - A new hybrid algorithm for multi-objective distribution feeder reconfiguration considering reliability
hossein lotfi -
Open Access Article
107 - Distribution Network Reconfiguration Considering Energy Storage Devices Based on Binary Particle Swarm Optimization
Mostafa Karimi Mohsen Simab -
Open Access Article
108 - Load Balancing Distribution Network Reconfiguration Based on Binary Particle Swarm Optimization
Mostafa Karimi Mohsen Simab Mehdi Nafar -
Open Access Article
109 - Reducing Displacement of Spring Mass in Active Vehicle Suspension System Using Sliding Mode Controller Based on Disturbance Observer
Mohammad Vatankhah Mohammad Yousefi Sayyed Mohammad Mehdi Mirtalaei Zahra AlaleThe main cause of oscillation during the movement of the vehicle is the unevenness of the road. Therefore, in order to maintain the stability of the car in swing states, the suspension system plays an essential role. Therefore, the active suspension system is used to re MoreThe main cause of oscillation during the movement of the vehicle is the unevenness of the road. Therefore, in order to maintain the stability of the car in swing states, the suspension system plays an essential role. Therefore, the active suspension system is used to replace the conventional passive suspension system, to improve comfort and smoothness. To reduce the displacement of the spring mass in the active vehicle suspension system, a high-order sliding mode controller is proposed in this paper. Uncertainty of system parameters, nonlinear characteristic of damping and spring, load changes and unknown path disturbance are estimated by disturbance observer. The controller only needs the information of the spring mass state variables and therefore does not need separate sensors to measure the suspension mass state variables. Particle swarm optimization algorithm has been used to determine the control parameters. The efficiency of the proposed method has been shown using simulation in MATLAB software and the results have been compared with the passive suspension system. Manuscript profile -
Open Access Article
110 - Synchronizing of Smart Homes in Microgrids using Whale Optimization Algorithm
Farhad Nourozi Navid Ghardash khaniThe household energy management system (HEMS) can optimally schedule home appliances for transferring loads from peak to off-peak times. Consumers of smart houses have HEM, renewable energy sources and storage systems to reduce the bill. In this article, a new HEM model MoreThe household energy management system (HEMS) can optimally schedule home appliances for transferring loads from peak to off-peak times. Consumers of smart houses have HEM, renewable energy sources and storage systems to reduce the bill. In this article, a new HEM model based on the time of usage pricing planning with renewable energy systems is proposed to use the energy more efficiently. The new meta-heuristic whale optimization algorithm (WOA) and the common meta-heuristic of particle swarm optimization (PSO) are used to achieve that. To improve the performance, a mapping chaos theory (CWOA) is proposed. Also, an independent solar energy source is used as a support of the microgrid to achieve a better performance. It is concluded that the energy saving achieved by the proposed algorithm is able to decrease the electricity bill by about 40-50% rather than the WOA and PSO methods. The proposed system is simulated in MATLAB environment. Manuscript profile -
Open Access Article
111 - Mix proportioning of high-performance concrete by applying the GA and PSO
Alireza rezaee mohamad reza hasani ahangar -
Open Access Article
112 - Per Unit Coding for Combined Economic Emission Load Dispatch using Smart Algorithms
Naser Ghorbani Ebrahim Babaei Sara Laali Payam Farhadi -
Open Access Article
113 - Optimization of grid independent diesel-based hybrid system for power generation using improved particle swarm optimization algorithm
Akbar Maleki Fathollah Pourfayaz -
Open Access Article
114 - Solving Economic Dispatch in Competitive Power Market Using Improved Particle Swarm Optimization Algorithm
Hossein Lotfi Ali Dadpour Mahdi Samadi -
Open Access Article
115 - Artificial Intelligence Based Approach for Identification of Current Transformer Saturation from Faults in Power Transformers
A. R Moradi Y Alinejad Beromi K Kiani Z Moravej -
Open Access Article
116 - Efficient Data Mining with Evolutionary Algorithms for Cloud Computing Application
Hamid Malmir Fardad Farokhi Reza Sabbaghi-Nadooshan -
Open Access Article
117 - A Triple State Time Variant Cost Function Unit Commitment with Significant Vehicle to Grid Penetration
Morteza Aien Mahmud Fotuhi Firuzabad -
Open Access Article
118 - Optimal Allocation of the Distributed Active Filters Based on Total Loss Reduction
Vahid Chakeri Mehrdad Tarafdar Hagh -
Open Access Article
119 - Frequency Control of Isolated Hybrid Power Network Using Genetic Algorithm and Particle Swarm Optimization
Mahdie Hasanpour Qadikolai Sina mohammadi -
Open Access Article
120 - Particle Swarm Optimization with Smart Inertia Factor for Combined Heat and Power Economic Dispatch
Naser Ghorbani Babak Adham Payam Farhadi -
Open Access Article
121 - Stock portfolio optimization using Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) under Conditional Value at Risk (CVaR)
Arezou Karimi sara goodarzi dahriziThe choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selec MoreThe choice of stock portfolio is a special issue in the field of investment. Given the wide range of options in the stock market, one of the major concerns of investment groups is the optimal allocation of assets. Therefore, most of these collections use portfolio selection models. The conditional value at Risk, which is one of the models of portfolio selection, follows the Quadratic Programming. Given that Quadratic Programming requires extensive computations, the use of metaheuristic algorithms in solving these problems increases the speed and accuracy of computations. The aim of this study is to minimize the Conditional Value at Risk by using two algorithms of Imperialist Competitive Algorithm and Particle Swarm Optimization. Therefore, using 800 days of data from 12 companies listed on the Tehran Stock Exchange in the period of 2/5/92 to 1/28/98, portfolio has been formed, and the weight of each stock in the optimal portfolio and the risk and return of the portfolio has been calculated using MATLAB2018 software. Then, using SPSS software, the average difference between risk and return of the two algorithms was tested.The results showed that the risk and return of the two algorithms were not statistically significant,. Manuscript profile -
Open Access Article
122 - Provide a model for predicting noisy stock price time series using singular spectrum analysis, support vector regression with particle swarm optimization and compare it with the performance of wavelet transform, neural network, moving average self-regression process and polynomial regression
Shaban Mohammadi Hadi Saeidi abdolhosein talebi najafabadi ghasem elahi shirvanIn this research, a model for analyzing and predicting the noisy financial time series of stock prices using singular spectrum analysis and support vector regression along with particle swarm optimization is presented. Thus, the time series of closed prices of 140 share MoreIn this research, a model for analyzing and predicting the noisy financial time series of stock prices using singular spectrum analysis and support vector regression along with particle swarm optimization is presented. Thus, the time series of closed prices of 140 shares of companies in different industries per minute per day for the period from 28 May to 11 June for the years 1392 to 1398 was examined separately from the Tehran Stock Exchange. Also, the performance of the proposed model was compared with the performance of four wavelet transform models with neural network, moving average regression process, polynomial regression and naïve model. Mean absolute error, mean absolute error percentage, and mean square root of error were used as the main performance criteria. The results show that the performance of the proposed model for analyzing and predicting noisy financial time series based on mean absolute error, mean absolute error percentage and mean square root of error is better than other models (including: wavelet transform, moving average self-regression, regression Polynomial is the naïve model). Manuscript profile -
Open Access Article
123 - Examining the Efficiency Models, Conditional Value at Risk and Mean Absolute Deviation and Particle Swarm Optimization Algorithm under CVAR and MAD risk criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
dariuosh adinehvand Ebrahim ali Razini Rahmani Mahmod khoddam Fereydon Ohadi alhamsadat hashemizadehChoosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including MoreChoosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of absolute deviation risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the deviation - absolute risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results show that the PSO model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more return in PSO-MAD criteria and therefore it is a better method to solve such portfolio optimization problems. Manuscript profile -
Open Access Article
124 - Designing a credit portfolio optimization model in the banking industry using a meta-innovative algorithm
ali asghar tehrani poor Ebrahim Abbasi Hosein Didehkhani arash naderianThe purpose of this study is to design a credit portfolio optimization model in the banking industry using a meta-innovative algorithm. Risk is one of the basic concepts in financial markets that has a certain complexity. Due to the lack of a clear picture of risk reali MoreThe purpose of this study is to design a credit portfolio optimization model in the banking industry using a meta-innovative algorithm. Risk is one of the basic concepts in financial markets that has a certain complexity. Due to the lack of a clear picture of risk realization, financial markets need risk control and management approaches. The present study is a descriptive survey in terms of data collection and applied in terms of purpose. The statistical population of this research includes all facility files of the last 10 years as well as the financial statements of Ansar Bank branches affiliated to Sepah Bank, which were selected by census method. The risk criteria used in the models are: fuzzy risk value, absolute value of fuzzy downward deviations and half entropy. Research models were implemented using multi-objective particle swarm optimization algorithm. The software used in conducting research is MATLAB software. The results show that the performance of the fuzzy risk-averaged model is better than the other two models in evaluating optimal portfolios. Therefore, the use of the above model in credit basket optimization is recommended. Manuscript profile -
Open Access Article
125 - Optimization on ELM network using Particle swarm Optimization Algorithms and OSELM to predict the industry index in Tehran Stock Exchange
, benyamin hakimzadeh ehsan Taiebysani Mahdi Saeidi KoushaThere have always been two approaches to forecasting in financial markets: traditional and intelligent approaches. In the traditional method, this forecasting is based on statistical models and in the intelligent method is based on artificial intelligence models. Tradit MoreThere have always been two approaches to forecasting in financial markets: traditional and intelligent approaches. In the traditional method, this forecasting is based on statistical models and in the intelligent method is based on artificial intelligence models. Traditional methods mainly use linear patterns to model market behavior, while the main advantage of smart models is the ability to learn and model nonlinear behaviors in the market. It has always been a question of which methods can better model market behavior, and despite the many models that have been proposed for forecasting, there is still an attempt to build a model that can use more effective variables for forecasting. Continues to be able to take into account factors such as time, risk and return. In this research, we have used the neural network to predict the industry index. This is done by ELM neural network using two optimization methods OSELM and PSO. The results show that the prediction accuracy of these two methods is not significantly different from each other, but in terms of execution time, the OSELM neural network algorithm has performed much better and faster. Manuscript profile -
Open Access Article
126 - Selection and Portfolio Optimization by Genetic Algorithms using the Mean Semi-Variance Markowitz Model
Asgar Pakmaram jamal Bahri Sales Mostafa ValizadehOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not onl MoreOne of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society. In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this. In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk. Since the objective function of genetic algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of these algorithms in the selection of the optimal portfolio. Manuscript profile -
Open Access Article
127 - Stock Portfolio optimization: Effectiveness of particle swarm optimization and Markowitz model
Ali Bayat lida asadiThe purpose of the portfolio management is the portfolio selection, the portfolio that acts as guidance to investors in order to achieve to maximum efficiency. In this study for portfolio selection, particle swarm optimization and Markowitz model are used and a comparis MoreThe purpose of the portfolio management is the portfolio selection, the portfolio that acts as guidance to investors in order to achieve to maximum efficiency. In this study for portfolio selection, particle swarm optimization and Markowitz model are used and a comparison was made between them. Introducing the model to select a portfolio for investors who can make the right choice with evaluation of that model is of our objectives in this study. For this purpose, literature and various studies are verified and a set of measures with regard to the purpose of the research was collected. Among the companies listed on the Tehran Stock Exchange, 65 companies were selected as sample for the period 2009 to 2013 and were analyzed as a statistical sample. To analyze the data, first the data is collected and categorized in software EXCEL and after doing calculations were analyzed using MATLAB software.TThe results of this research showed that the particle swarm optimization has a fewer errors in the selection of optimal portfolio compared with Markowitz model. The most important suggestion for future research is to compare the particle swarm optimization with other models of optimization such as, colonial competition, meta-heuristic, arbitrage model and etc. Manuscript profile -
Open Access Article
128 - Explaining the model of earning management measurement using an intelligent hybrid method of neural networks and meta heuristic algorithms (Genetic and particle swarm optimization)
Eghbal Ghaderi piman amini Iraj Noravesh Ata Mohammadi MoqrnyUndrestanding the earning management for the users of accounting information due to performance evaluation, profitability forecast and detrmining the value of the company is very important.The purpose of this research is to estimate the a model for earning management us MoreUndrestanding the earning management for the users of accounting information due to performance evaluation, profitability forecast and detrmining the value of the company is very important.The purpose of this research is to estimate the a model for earning management using neural network model and then the use of Genetic Algorithm, and Particle Swarm Optimization to find a better combination of input data, so that it can optimize the initial model. For this purpose, 28 effective variables were used in the from of four groups (Financial, managerial, corporative and audit) during the years 2010 to 2016 in the companies admitted to the Tehran stock Exchange. The results showed that application of this algorithm has increased the efficiency of the model.Also, the evaluation of the performance of neural network patterns suggests the absolute superiority of this pattern compared to the time linear method (LR).Combined method (A-PSO) and (A-GA)by identifying four optimal variables respectively precision forecast, shareholding of major shareholders, company size and the ratio of the quality of earning management are carefully predicted respectively (%95/59) and (%94/75). In addition to the above mentioned intelligent methods, by improving correlation coefficient and error squares mean criterion compared to linear methods (LR) and neural network method (ANN) in predicting group results, management and corporate features are more efficient. Manuscript profile -
Open Access Article
129 - Improving the Performance of Adaptive Neural Fuzzy Inference System (ANFIS) Using a New Meta-Heuristic Algorithm
Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi hafshejani -
Open Access Article
130 - ANALYSIS OF A DISCRETE-TIME IMPATIENT CUSTOMER QUEUE WITH BERNOULLI-SCHEDULE VACATION INTERRUPTION
P. Vijaya Laxmi K. Jyothsna -
Open Access Article
131 - The Modeling of Exchange Rate Predict in Iran by Using Neural Network Based on Genetic Algorithms and Particle Swarm Algorithm
ali jamali saeed daie karimzadehIn recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weakn MoreIn recent years the use of artificial intelligence techniques in the financial and investment markets instead of customary quantitative methods has been increasing and gives better performance towards classic methods usually. Artificial Neural Network (ANN), has weaknesses points despite its enormous benefits also. In this study, in order to overcome the weaknesses of the network consists of combining artificial intelligence methods with Evolutionary algorithms, means of artificial neural network combined with genetic algorithm (GA) and Particle Swarm algorithm (PSO) to model and daily predict of nominal exchange rates or the exchange rate dollar by Rial in Iran in the period 21.03.2013 to 22.12.2019 is used. This combined model with neural networks method as one artificial intelligence model according to the criteria of MSE , RMSE, MAE, U.Theil compared. The results of this research show the superiority of synthetic neural network model -Particle Swarm algorithm compare to other models of investigation. Manuscript profile -
Open Access Article
132 - Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)
Majid Mohammadhosseini -
Open Access Article
133 - Examining the efficiency of optimization models of multi objective genetic algorithm and particle swarm algorithm under the risk criteria of conditional value at risk and mean smai variance in determining the optimal stock portfolio
Dariush Adinehvand Ebrahim Ali Razini Rahmani Mahmoud Khoddam Fereydoun Ohadi Elham Sadat HashemizadehObjective: The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.Research Methodology: Attaini MoreObjective: The goal is to select an optimal portfolio of stocks by allocating capital among various investment opportunities in the stock market to achieve maximum return at a specified level of risk. This constitutes an efficient portfolio.Research Methodology: Attaining an efficient portfolio involves solving an optimization problem. There are numerous techniques and tools available to solve this issue. In this study, 15 stocks from companies listed on the Tehran Stock Exchange, including symbols such as Khapars, Khazamiya, Vepasar, Foulad, Akhabar, Kegel, Femli, Tapiko, Sepaha, Fazer, Fakhas, Shohbaran, Shefan, Qamro and Qathabat, were selected using cluster sampling. First, the daily returns of these stocks were calculated over a 5-year period from 2015 to 2020 (1183 days). The risk of the optimal investment portfolio was then calculated using the Mean-Semi Variance and Conditional Value at Risk models. These two criteria were compared using a classic solution method. Subsequently, the output data obtained from these calculations were compared using MATLAB software, employing the Particle Swarm Optimization algorithm under the Mean-Semi Variance risk criterion and the Genetic Algorithm under the Conditional Value at Risk criterion.Findings: The results of this study indicate that the meta-heuristic Particle Swarm Optimization method yields a higher portfolio return ratio compared to the Genetic Algorithm in the Mean-Semi Variance risk criterion.Originality / Value: This research utilizes multi-objective genetic algorithms and Particle Swarm Optimization, which are intelligent and novel algorithms, to minimize the objective function value using Conditional Value at Risk and Mean-Semi Variance criteria. These algorithms optimize the return and risk ratios of the stocks in the investment portfolio with the highest possible accuracy. Additionally, the efficiency comparison of these models using MATLAB software contributes an innovative aspect to this study. Manuscript profile -
Open Access Article
134 - شبیه سازی حرکت پای انسان با مکانیزم یک درجه آزادی
دامون بختیاریان هادی همایی امین ملکی زاده مراد شهبازی تک آبینیاز به شبیه سازی الگوی حرکت پای انسان محققان و مهندسان را به سوی ارائه الگوهای متفاوت برای توصیف این حرکت کرده است. در این میان راه حل های بهینه از لحاظ مصرف انرژی و دقت و غیره از اهمیت بالایی برخوردارند. در این مقاله تلاش شده تا با طراحی یک مکانیزم کاملا دو بعدی شش می Moreنیاز به شبیه سازی الگوی حرکت پای انسان محققان و مهندسان را به سوی ارائه الگوهای متفاوت برای توصیف این حرکت کرده است. در این میان راه حل های بهینه از لحاظ مصرف انرژی و دقت و غیره از اهمیت بالایی برخوردارند. در این مقاله تلاش شده تا با طراحی یک مکانیزم کاملا دو بعدی شش میله ای با یک درجه آزادی به گونه ای که کمترین خطا را با حالت طبیعی راه رفتن پای انسان داشته باشد، راه حل جدیدی ارائه داده شود . ضمن اینکه یافته های این مقاله فرآیندی برای بهینه سازی مکانیزم های چندمیله ای ارائه می کند تا بتوان آن را در هر زمینه ی دیگری مانند ساخت پروتز پای انسان بکار برد. در اینجا از الگوریتم بهینه­سازی اجتماع ذرات استفاده شد. نتایج کار حاصل از بهینه سازی با داده های آزمایشگاهی پای انسان مقایسه شده است. نتایج نشان می دهد که مکانیزم شش میله ای پیشنهادی، ضمن بهینه کردن بسیاری از پارامترهای حرکتی به خوبی قادر به شبیه سازی حرکت پای انسان است. Manuscript profile -
Open Access Article
135 - Optimal Placement of Static VAR Compensator to decrease Loadability Margin by a Novel Modified Particle Swarm Optimization Algorithm
Maryam Falah nezhadnaeini Mohammad Hajivand Reihaneh Karimi Mohammad Karimi
Sanad
Sanad is a platform for managing Azad University publications
Links
Related Centers
Technical Support
Official pages
The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024