Particle swarm Algorithm

Open Access Article

1 - Predicting local scour depth of bridge piers using hybrid particle swarm optimization and gray wolf optimizer
Mehran Sarabi Seyed Abbas Hosseini

Construction 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 More

Construction 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 Hafshejani

10.30495/jmfr.2022.55572.2264

Due 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 More

Due 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 - Selection and Portfolio Optimization by Mean–Variance Markowitz Model and Using the Different Algorithms
Jamal Bahri Sales Askar Pakmaram Mostafa Valizadeh

One 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 More

One 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

4 - Presenting a multi-objective model for locating warehouses through particle swarm optimization algorithm in Artaville Tire
Hojatolah Derakhshan Hasan Mehrmanesh Arefe Fadavi

10.30495/jisds.2007.21341

This 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 More

This 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

5 - usefulness of meta-heuristic algorithms on optimizing of the integrated risk in banking system
eskandar vaziri Farhad dehdar Mohamad reza abdoli

aim 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 More

aim 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

6 - Provide a Earnings Management forecasting model using ant colony and particle swarm algorithm algorithms
Vahid Yousefi HAMIDREZA KORDLOUIE faegh ahmadi mohammadhamed Khanmohammadi Dashti Nader

This 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 More

This 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

7 - Optimal Routing of Rocket Motion using Genetic Algorithm and Particle Swarm Optimization
Reza Tarighi M.H. Kazemi mohammad hosein khalesi

10.30486/admt.2022.1947306.1327

Manuscript profile

Open Access Article

8 - 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

10.22034/amfa.2021.1910900.1493

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Open Access Article

9 - 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

10.22094/joie.2020.677889

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Open Access Article

10 - Application of Swarm-Based Optimization Algorithms for Solving Dynamic Economic Load Dispatch Problem
Alireza Khosravi Mohammad Yazdani-Asrami

Manuscript profile

Open Access Article

11 - Selection and Portfolio Optimization by Genetic Algorithms using the Mean Semi-Variance Markowitz Model
Asgar Pakmaram jamal Bahri Sales Mostafa Valizadeh

One 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 More

One 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

12 - Improving the Performance of Adaptive Neural Fuzzy Inference System (ANFIS) Using a New Meta-Heuristic Algorithm
Mehdi Khadem Abbas Toloie Eshlaghy Kiamars Fathi hafshejani

10.30495/ijm2c.2023.1955549.1251

Manuscript profile

Open Access Article

13 - The Modeling of Exchange Rate Predict in Iran by Using Neural Network Based on Genetic Algorithms and Particle Swarm Algorithm
ali jamali saeed daie karimzadeh

In 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 More

In 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

14 - 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 Hashemizadeh

10.30495/afi.2023.1988982.1233

Objective: 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 More

Objective: 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

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