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Open Access Article
1 - Multi-Objective Operation of the Distribution System Including Wind Turbines, Taking Into Account the Minimization of Environmental Pollution in the Network
Reza SedaghatiIntroduction: The ever-increasing growth of consumption loads and the necessity of proper, timely and reliable supply of power networks require a new attitude in the optimal operation of power systems and lines more than ever. On the other hand, in recent years, there h MoreIntroduction: The ever-increasing growth of consumption loads and the necessity of proper, timely and reliable supply of power networks require a new attitude in the optimal operation of power systems and lines more than ever. On the other hand, in recent years, there has been a lot of support for distributed generation sources based on renewable energies, especially wind turbines. One of the main problems of wind turbines is the problem of extreme wind fluctuations and the dependence of output power on wind speed. Parallel to this problem, in the discussion of network management, the error caused by forecasting the consumption load in the future can also lead to the problem becoming more and more difficult. One of the suitable techniques without initial cost is the method of network topology reconfiguration with the objective of improving the network situation. Materials and Methods: Therefore, in this research, in order to investigate the problem of reconfiguration of the distribution network with the presence of wind turbine sources, a new method for their simultaneous management has been presented. A multi-objective function is considered to reduce the active losses of the network, reduce the overall costs of the network, improve the voltage profile of the existing buses, and reduce the total emissions generated by the network, which uses the firefly optimization algorithm to minimize it. Results and Discussion: Solving the problem of renewing the structure by considering the uncertainty caused by wind turbines is considered. The presence of wind resources in the network has been able to significantly reduce the objective functions. Conclusion: The results of this research showed that the American land reclamation method is better than the other mentioned methods because it has estimated more flow in flood calculation. An important result of flood zoning resulting from the breaking of Tangab dam is that the urban area of Firozabad is safe from this flood and the villages are not flooded as far as the studied area is concerned. Based on the obtained results, it can be concluded that the result of the possible failure of the dam, based on this research, the flood caused by the failure of the dam, except for 1 hectare of the industrial sector, which is a very small area, will cause damage only to agricultural lands. Manuscript profile -
Open Access Article
2 - A hybrid meta-heuristic algorithm based on ABC and Firefly algorithms
azita yousefi bita amirshahi -
Open Access Article
3 - Dynamic Replication based on Firefly Algorithm in Data Grid
mehdi Sadeghzadeh -
Open Access Article
4 - The Predication of Stock Price Using Firely Algorithm
Ali Bayat Zeynab BagheriIn this study, the prediction of stock price of some manufactors listed in Tehran stock market and some others, using firefly algorithm has be done.In this study firstly, we used 16 variables for a period of 3 years (1388-1392) to educating the algorithm and after that MoreIn this study, the prediction of stock price of some manufactors listed in Tehran stock market and some others, using firefly algorithm has be done.In this study firstly, we used 16 variables for a period of 3 years (1388-1392) to educating the algorithm and after that , we used educated algorithm to predict the stock price of manufactors with 12 variables. the relative fault was calculated for stock prices for before and after prediction. the average of This fault isless than %6 and the result is that the stock price prediction using fire fly algorithm is achievable and possible. Manuscript profile -
Open Access Article
5 - Application of Hybrid Model of Artificial Neural Networks and Firefly Algorithm to Predict the Amount of TDS in River Water
Farahnaz Sabzevari Behrouz Yaghoubi Saeid ShabanlouBackground and Aim: Estimation and forecasting of qualitative parameters along with quantitative parameters of water alongside the river to make correct managerial decisions is one of the objectives of managers and planners of the water industry should be accurately sim MoreBackground and Aim: Estimation and forecasting of qualitative parameters along with quantitative parameters of water alongside the river to make correct managerial decisions is one of the objectives of managers and planners of the water industry should be accurately simulated. Most of the models for qualitative parameter estimations require very large input parameters that are either difficult to access or require much time and money to determine. Therefore, the use of data-driven models in this field has been developed to save time and money.Method: In this paper, the application of artificial neural networks and its combination with the firefly algorithm to predict the amount of Total dissolved solids (TDS) of water in the Gavehrood River located in Iran, Kermanshah has been trained and validated. with this purpose, water quality data of hydrometric station upstream of the Gavoshan reservoir dam are used for the statistical period (1991-2010). Based on different inputs, the multilayer perceptron (MLP) artificial neural network and its combination with the firefly algorithm are tested. The best algorithm of the inputs, the number of hidden layers and the number of neurons in each layer in the artificial neural network are determined. The input data imported to the models include the flow rate (Q), Sodium (Na), Magnesium (Mg), Calcium (Ca), Sulfate (So4), Chloride (Cl), Bicarbonate (Ho3), Electrical conductivity (EC) and Total Dissolved Solides of the river in the previous period (TDSt-1) and the output data of TDS. The number of hidden layers is obtained to be 1 and the number of hidden layer neurons is achieved to be 9. Also, the neural network function in this study is considered as a waterfall type and the results are compared by combining artificial neural networks with the firefly algorithm. The model outputs are compared with measurement data using the error measurement criteria.Results: In this regard, the values of the used error evaluation indices including the observed standard deviation (RSR), Nash Sutcliffe coefficient (NSC), correlation coefficient (R) and root mean square error (MSE) for artificial neural network are yielded 0.154, 0.976, 0.989 and 25.27, respectively and in the case of the neural network combination with the firefly algorithm, are achieved to be 0.129, 0.983, 0.992 and 17.8, respectively.Conclusion: Therefore, the performance of the hybrid method of artificial neural networks by using the firefly algorithm in predicting TDS is more appropriate than artificial neural networks. Manuscript profile -
Open Access Article
6 - Optimizing operation of reservoir for agricultural water supply using firefly algorithm
Seyed Mohammad Hosseini-Moghari Mohammad Ebrahim BanihabibThe largest amount of water in Iran is used in agricultural sector. Thus, efficient use of water in this sector will be significantly effective in maintaining water resources and optimum use of available water. In many regions, surface reservoirs are responsible for pro MoreThe largest amount of water in Iran is used in agricultural sector. Thus, efficient use of water in this sector will be significantly effective in maintaining water resources and optimum use of available water. In many regions, surface reservoirs are responsible for providing water to downstream agriculture. Optimal operation of reservoir is one of the major parts of surface water resource optimization. So far, several optimization approaches have been used, among them, the most popular methods are Evolutionary Algorithms. In this study, Firefly Algorithm (FA), as a new method, was proposed for optimal operating of Bazoft reservoir. The operation modeling was carried out for a period of 120 months related to 1986 to 1995 years. The considered objective function was defined as minimizing the sum of squared differences between the demands and the release from the reservoir divided by maximum demand during operation. The model performance was examined compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). These two mentioned algorithms are known as common and standard methods.The results indicated that firefly algorithm can better perform than other methods. The mean value of the objective function of this method was 0.408, and the mean of the objective function for the GA and PSO were 0.618 and 0.913, respectively. In addition, FA has created less deficiency values and milder deficiency compares to GA and PSO. Manuscript profile -
Open Access Article
7 - Quantitative structure–activity relationship study for predicting activity of some medicine compounds by firefly algorithm
mehdi nekoei Fatemeh ShamsQuantitative structure–activity relationship models were developed for predicting activity of a series of medicine compounds such as pyridine derivatives. The suitable set of the molecular descriptors was calculated and the important descriptors using the variable MoreQuantitative structure–activity relationship models were developed for predicting activity of a series of medicine compounds such as pyridine derivatives. The suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections of the stepwise (SW) and the firefly algorithm (FFA) were selected. The predictive quality of the QSAR models was tested for an external set of compounds by multiple linear regression (MLR). Statistical parameters for SW-MLR and FF-MLR were R2 train = 0.835, 0.859, RMSEP = 0.620, 0.786, REP= 10.72, 10.52 respectively. A comparison between the attained results indicated the superiority of the firefly algorithm over the stepwise method in the feature selection. The predicted results of this study can be used to design new inhibitors of melanoma anti-cancer. Manuscript profile -
Open Access Article
8 - FA-ABC: A Novel Combination of Firefly Optimization Algorithm and Artificial Bee Colony for Mathematical Test Functions and Real-World Problems
Ali reza Shafiee sarvestany Mohammadjavad Mahmoodabadi -
Open Access Article
9 - Evaluating the Performance of Intelligent Traffic Signals Based on Firefly Algorithm Applied in an Adaptive Control System
fariba jabbari Mehdi Fallah TaftiIntroductionIn this article, the performance of adaptive traffic signal control systems based on an artificial intelligence technique, namely Firefly Algorithm, for traffic control at urban intersections has been investigated. In order to check the proposed algorithm, t MoreIntroductionIn this article, the performance of adaptive traffic signal control systems based on an artificial intelligence technique, namely Firefly Algorithm, for traffic control at urban intersections has been investigated. In order to check the proposed algorithm, the required data were first collected from two intersections in Yazd city. These intersections were then simulated using AIMSUN traffic simulator software and calibrated and validated under existing conditions. In the next step, the adaptive control system based on the proposed Firefly algorithm was developed and then used in simulated intersections and its performance was compared with pre-time control system in terms of intersection traffic capacity and vehicle queue length at the entry approaches. The t-test was used for a more scientific investigation. For this reason SPSS software was used as one of the most widely used statistical software to perform this test.MethodIn order to concurrently optimize vehicle departure volume or throughputat and queue length at the entry approaches of each intersection, the Firefly algorithm was used to to develop a multi-objective adaptive traffic signal control logic and appropriately distribute the effective green time in each cycle between the entry approaches. ResultsThe simulation results indicated a lower average queue length and higher throughput when the proposed adaptive model was compared with pre-time model at both intersections. The t-test results showed that the adaptive traffic signal control method has resulted in a significant lower average queue length than the pre-time time control at one of the intersections with p-value equal to 0.002. However, this improvement was not statistically significant for the other intersection. Moreover, the t-test results on the average flow departure volume measure for both intersections indicated a significant improvement with p-value equal to 0.000. when the proposed adaptive method was compared to the pre- time method.DiscussionAccording to the results, the proposed adaptive model showed better overall performance in the scope of this research than the pre-time control method. The results indicate that the performance of adpative signal controls could be enhanced when artificial intelligence techniques such as Firefly algorithm are used in the control logic and a multi-objective optimization approach is used. Manuscript profile -
Open Access Article
10 - A new algorithm for data clustering using combination of genetic and Fireflies algorithms
Mahsa Afsardeir mansoure AfsardeirIntroduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret MoreIntroduction: With the progress of technology and increasing the volume of data in databases, the demand for fast and accurate discovery and extraction of databases has increased. Clustering is one of the data mining approaches that is proposed to analyze and interpret data by exploring the structures using similarities or differences. One of the most widely used clustering methods is the k-means. In this algorithm, cluster centers are randomly selected and each object is assigned to a cluster that has maximum similarity to the center of that cluster. Therefore, this algorithm is not suitable for outlier data since this data easily changes centers and may produce undesirable results. Therefore, by using optimization methods to find the best cluster centers, the performance of this algorithm can be significantly improved. The idea of combining firefly and genetics algorithms to optimize clustering accuracy is an innovation that has not been used before.Method: In order to optimize k-means clustering, in this paper, the combined method of genetic algorithm and firefly worm is introduced as the firefly genetic algorithm.Findings: The proposed algorithm is evaluated using three well-known datasets, namely, Breast Cancer, Iris, and Glass. It is clear from the results that the proposed algorithm provides better results in all three datasets. The results confirm that the distance between clusters is much less than the compared approaches.Discussion and Conclusion: The most important issue in clustering is to correctly determine the cluster centers. There are a variety of methods and algorithms that performs clustering with different performance. In this paper, based on firefly metaheuristic algorithms and genetic algorithms a new method has been proposed for data clustering. Our main focus in this study was on two determining factors, namely the distance within the data cluster (distance of each data to the center of the cluster) and the distance that the headers have from each other (maximum distance between the centers of the clusters). In the k-means algorithm, clustering is not accurate since the cluster centers are selected randomly. Employing firefly algorithms and genetics, we try to obtain more accurate centers of the clusters and, as a result, correct clustering. Manuscript profile -
Open Access Article
11 - Predict the Stock price crash risk by using firefly algorithm and comparison with regression
Serveh Farzad Esfandiar Malekian Hossein Fakhari Jamal Ghasemi -
Open Access Article
12 - Providing a Recommendation System for Recommending Articles to users using Data Mining Methods
Reza Molaee fard Payam Yarahmadi -
Open Access Article
13 - A Hybrid Grey based Two Steps Clustering and Firefly Algorithm for Portfolio Selection
farshad faezy razi Naeimeh Shadloo -
Open Access Article
14 - Multi Objective Optimization of Heat Pipe Using Firefly Algorithm
hossein amoozad khalili gholamreza salehi seyed mohsen momeni majid eshagh nimvari -
Open Access Article
15 - Determining the Optimum Investment Portfolios in the Iranian Banking Network Base on Bi-level Game using the Markowitz Optimization Model by Firefly Algorithm
Mehdi Memarpour Ashkan Hafezalkotob Mohammad Khalilzadeh Abbas Saghaei Roya Soltani -
Open Access Article
16 - Optimization of Stand-alone Hybrid PV/Wind/Fuel-Cell System Considering Reliability Indices Using Cuckoo Optimization and Firefly Algorithm
Mehdi Rezaei محمود قنبری -
Open Access Article
17 - A new method for detection of breast cancer in mammography images using a firefly algorithm
Ghazal Mardanian Neda BehzadfarBreast cancer is one of the most common cancers among women. Many times, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a g MoreBreast cancer is one of the most common cancers among women. Many times, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a gold standard for over 40 years in diagnosing breast diseases. In recent years, artificial intelligence systems have been the focus of much attention in preventing the subjective analysis of mammograms and physicians by radiologists and enhancing the accuracy of breast cancer detection. In this study, combining the firefly algorithm and applying appropriate image processing to detect breast cancer in mammographic images has been investigated. In this paper, mammographic images in the DDSM dataset were used. Three performance metrics such as sensitivity, specificity and accuracy (93.4%, 91%, 95%) were used to analyze the detection performance. The proposed work shows better performance when compared to existing work in literature. Manuscript profile -
Open Access Article
18 - Utilizing Firefly Algorithm-Optimized ANFIS for Estimating Engine Torque and Emissions Based on Fuel Use and Speed
Mahmut Dirik -
Open Access Article
19 - الگوریتم بهینه سازی چندهدفه کرم شب تاب برای طراحی جانمایی کارگاه ساختمانی
Abolfazl Ghadiri داود صداقت شایگان علی اصغر امیرکاردوستاهمیت ایمنی در طرح چیدمان سایت ساخت و ساز یک نیاز ضروری برای بهبود مدیریت پروژه ساختمانی است. در مطالعات قبلی تابع هدف، ایمنی بدون تجزیه و تحلیل عوامل خطر در نظر گرفته شده است. فراابتکاری ها به طور گسترده ای برای حل مسائل برنامه ریزی چیدمان سایت ساخت و ساز استفاده می شو Moreاهمیت ایمنی در طرح چیدمان سایت ساخت و ساز یک نیاز ضروری برای بهبود مدیریت پروژه ساختمانی است. در مطالعات قبلی تابع هدف، ایمنی بدون تجزیه و تحلیل عوامل خطر در نظر گرفته شده است. فراابتکاری ها به طور گسترده ای برای حل مسائل برنامه ریزی چیدمان سایت ساخت و ساز استفاده می شود. الگوریتم کرم شب تاب (FA) به عنوان روش بهینه سازی چند هدفه برای طراحی و بهینه سازی دو تابع هدف ایمنی و هزینه کل استفاده می شود. توابع هدف ایمنی (به دلیل خطرات بالقوه ناشی از منابع خطرناک و جریان های متقابل) اتصال تأسیسات موقت با در نظر گرفتن کاهش هزینه کل. یک مطالعه موردی برای پی بردن به دقت مدل پیشنهادی ارائه شده است. در نهایت، عملکرد دو الگوریتم فراابتکاری به نامهای الگوریتم فایرفلای (FA) و بهینهسازی کلونی مورچهها (ACO) از نظر اثربخشی در حل مشکل طراحی سایت ساختوساز مورد مقایسه قرار گرفتهاند. نتایج نشان می دهد که FA بهتر از الگوریتم ACO عمل می کند. Manuscript profile -
Open Access Article
20 - An Optimized Firefly Algorithm based on Cellular Learning Automata for Community Detection in Social Networks
Hasan Rabani Farhad Soleimanian Gharehchopogh -
Open Access Article
21 - A Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Global Optimization
Sosan Sarbazfard Ahmad Jafarian -
Open Access Article
22 - NMFA: Novel Modified FA algorithm Based On Firefly Recent Behaviors
Fatemeh Jafarnejad Rezaiyeh Kambiz Majidzadeh -
Open Access Article
23 - Improving Reliability through Selecting Data in Grid Distributed System and Comparing it with Other Presented Algorithms
Sedigheh Navaezadeh Iman Zangeneh Elham Tavakol -
Open Access Article
24 - An Optimal Similarity Measure for Collaborative Filtering Using Firefly Algorithm
Fatemeh Shomalnasab Mehdi Sadeghzadeh Mansour Esmaeilpour -
Open Access Article
25 - Solving Fractional Programming Problems based on Swarm Intelligence
Osama Abdel Raouf Ibrahim M. Hezam -
Open Access Article
26 - Investigating the effects of types of cash flow and controlling shareholders on the relationship between profit management and financial performance to predict financial bankruptcy (firefly algorithm)
Gurban Heki Behrouz Sadeghi Amroabadi Seyyed Mohammad Reza DavodiAbstract Companies need sufficient resources to continue operating, including sufficient cash to pay lenders. If the company does not have enough ability to acquire resources to meet its needs, it will suffer financial helplessness. When faced with financial helplessne MoreAbstract Companies need sufficient resources to continue operating, including sufficient cash to pay lenders. If the company does not have enough ability to acquire resources to meet its needs, it will suffer financial helplessness. When faced with financial helplessness, companies manipulate accounting profit as one of the performance evaluation items. In this situation, the management manages the profit by manipulating the accounts, the purpose of which is to give good information and news to the capital market, in order to prevent the decrease of the company's value. If the accounts are manipulated, the existence philosophy of the financial statements will be damaged and their reliability will be lost. Therefore, in the present research, the effects of cash flow types and controlling shareholders on the relationship between profit management and financial performance have been analyzed in order to predict financial bankruptcy in Tehran stock exchange companies. For this purpose, the data of 128 selected companies during the period of 1390 to 1398 have been used. Data analysis was done in two parts; In the first part, using the panel data regression method, the effect of cash flow types and controlling shareholders on the relationship between profit management and financial performance of companies has been estimated. The results of this part showed that the profit management variable had positive effects and the performance variables, controlling shareholders, capital cash flows, equity cash flows and free operating cash flows had a negative effect on the bankruptcy criteria of companies; In addition, it was observed that the types of cash flows examined in this research had a moderating effect on the relationship between profit management and corporate bankruptcy. Next, in the second part of the analysis, based on the coefficients obtained in the previous part and using the firefly algorithm, it is discussed. The results of this part also showed that the percentage of success of the firefly algorithm in predicting the bankruptcy of companies was equal to 98.12%. Based on this, it is suggested that policies based on control and preservation of various cash flows and the use of controlling shareholders be used to reduce the possibility of bankruptcy of companies. Manuscript profile -
Open Access Article
27 - A New Clustering Approach for Efficient Placement of Controllers in SDN using Firefly Algorithm
Azam Amin Mohsen Jahanshahi Mohammadreza Meybodi -
Open Access Article
28 - Financial Bankruptcy prediction using artificial neural network and firefly algorithms in companies listed in Tehran Stock Exchange
Mahdi Heidary Shokrollah Ziari seyed ahmad shayan nia Alireza Rashidi KemijanBy anticipating financial turmoil, it is possible to take the necessary precautions before financial distress occurs by managers and investors. This study compares two algorithms for prediction of bankruptcy using Artificial Neural Network (ANN) and Neural network optim MoreBy anticipating financial turmoil, it is possible to take the necessary precautions before financial distress occurs by managers and investors. This study compares two algorithms for prediction of bankruptcy using Artificial Neural Network (ANN) and Neural network optimized metaheuristic Firefly Algorithm (FA). To run test, first initial values are set for the network weights and biases and then during the optimization process, a population of different weights and biases is generated by FA algorithm. The conversion function used in the output layer is linear and for the middle layer a non-linear sigmoid function is selected. To conduct this research, the data of 79 companies listed on TSE during 2012 to 2015 were collected and analyzed statistically by backpropagation neural network and FA algorithms. The results show that FA, compared to ANN predicted the companies’ bankruptcy much better. Also, FA Algorithm maintains a good correlation between bankrupt and non-bankrupt companies, just like real data. Manuscript profile -
Open Access Article
29 - Designing a Credit Risk Management Model in the Network of after-sales service companies Using Financial Components of After-Sales Services and Metaheuristic Algorithms (Case study: Saipa's after-sales service company(Saipa Yadak))
Hamid reza Radmannejad Mohammad Ebrahim Mohammad Pourzarandi Mehrzad MinoueiThe type of customer service during the warranty is crucial for each complex. The purpose of customer service will be to meet the satisfaction of customers. Many components can contribute to accomplish this goal. One of the most important components is financial compone MoreThe type of customer service during the warranty is crucial for each complex. The purpose of customer service will be to meet the satisfaction of customers. Many components can contribute to accomplish this goal. One of the most important components is financial components. Today's world is a world of wide developments in all dimensions. The majority of companies are, more than ever, aware that the delivery of after-sales service is very effective in the loyalty and repetition of customer purchases. The intense focus on the quality of service causes the product to be valuable in terms of customers and their loyalty. Therefore, in this study, designing a credit risk management model for the for the Saipa Yadak Company and its Representatives Network using financial components of after-sales service and meta-heuristic algorithms was discussed. The sample studied in this research is the representatives of Saipa Company.The results showed that using financial components including, service cost, performance, good accounting, the amount of collateral and the amount of after-sales service agents have an impact on optimal credit risk management. Also, firefly algorithm and bee colony algorithm have the ability to predict the optimal management of credit risk using financial components. Manuscript profile -
Open Access Article
30 - Smart operating system based on technical parameters optimized with firefly algorithm
Fatemeh Asiaei Taheri Gholamreza zomorodian Mirfeiz FallahshamsThe main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done clo MoreThe main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done close to key reversal points. In recent years, various systems have been developed to identify these return points. Technical analysis tries to identify the time to enter and exit trades.In this article, we are trying to select the one with a higher success rate by using the technical rules according to the previous researches, and by using soft calculations, the decision parameters in the technical rules are improved using the firefly algorithm.The results of this model are compared with the results of using the standard parameters of the indicators and the results of the purchase and maintenance strategy. In order to validate the introduced trading system, we compared it with the results of the optimized intelligent system based on optics and genetic algorithm. The results of the research show that by optimizing the parameters of technical analysis indicators, the investment efficiency can be increased compared to the usual methods in the stock market and previous researches. Manuscript profile -
Open Access Article
31 - The use of Firefly Algorithm and Bayesian Regulation technique of optimized Artificial Neural Network to predict stock price in Iran Stock Market
seyyed alireza mosavi Afsaneh GholamiPredicting the future stock price has always been considered as an important issue by both buyers and sellers. Hence, Artificial Neural Network (ANN) was used in this study to develop a model pertaining to artificial intelligence in order to predict stock price in Iran MorePredicting the future stock price has always been considered as an important issue by both buyers and sellers. Hence, Artificial Neural Network (ANN) was used in this study to develop a model pertaining to artificial intelligence in order to predict stock price in Iran Stock Market. Since artificial neural networks should consist of the best network topology to achieve the highest performance, Firefly Algorithm (FA), a meta-heuristic Algorithm, was used to find the optimal structure of network. Finally, Bayesian regulation technique, rather than the conventional teaching techniques, was applied to maintain the more generalized network. In general, Data from three big companies: Iran Khodro Company, Shiraz Petrochemical Company, and Isfahan Steel Companywere gathered in span of three years. This paper profited from some parameters, including high price, low price, the opening price, closing price, EMA(5) ،EMA(10) ،RSI ،William R% ،Stochastic k% ،Stochastic D% و ،ROCas network inputs and benefited from the closing stock price in the next days as the neural network as well. After developing a model associated with each company, some parameters such as the root-mean-square error (RMSE), Standard Deviation of error(SD), Absolute average relative deviation (AARD), the regression coefficient (R2) as well as the graphical analysis of relative deviation have been used to examine the accuracy of the developed network. The outcomes of the analysis of the developed neural networks revealed that the mentioned models with great accuracy are able to predict stock price in the subsequent day for the corporations mentioned above. Manuscript profile -
Open Access Article
32 - Selection of Fuzzy Multi-Purpose Portfolios Based on the Cross-Sectional Return Model of Data Envelopment Analysis in Tehran Stock Exchange
fazel mohammadi nodeh Ahmad ayoub mousaabadi masoud asadi abbas babaei Shaban MohammadiFuzzy multifunctional sets reduce the need for accurate data for decision making. Data Envelopment Analysis is a theoretical framework for performance analysis and performance measurement. Fuzzy increases the application of data envelopment analysis. Measuring the perfo MoreFuzzy multifunctional sets reduce the need for accurate data for decision making. Data Envelopment Analysis is a theoretical framework for performance analysis and performance measurement. Fuzzy increases the application of data envelopment analysis. Measuring the performance of companies with the help of data envelopment analysis can help investors in choosing a company. In this paper, the problem of selecting fuzzy portfolios in a multipurpose framework is examined. A comprehensive model for selecting multi-purpose portfolios in fuzzy environment is presented using a semi-variance model and a model for analyzing information development with cross-sectional returns. Data from 40 companies accepted in Tehran Stock Exchange and trapezoidal returns of 40 sheets of securities and the data required for inputs and output of data envelopment analysis were obtained from financial statements of companies from the beginning of 1396 to the end of 1396. 16 financial parameters were used. Sharp ratio, cross-sectional return model in Sharp ratio and multi-purpose firefighting algorithm for solving multi-purpose stock optimization model was used. Analysis was done with MATLAB software. The results showed that the proposed method in this research is more suitable for selection of fuzzy multipurpose portfolio than other methods and provides better results for performance analysis, efficiency and company selection for investment. Manuscript profile -
Open Access Article
33 - Firefly Technique Based on Optimal Congestion Management in an Electricity Market
Jafar Bolouck Azari Noradin Ghadimi
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