meta-heuristic algorithms
Hossein Nikoo; Jamal Barzgari khanagha; Hamid Reza Mirzaei
Abstract
Purpose: Pair formation is an important step in pair trading that has only been examined manually or through numerical instructions. These methods fail in the multivariate mode and do not consider conflicting goals in the problem structure. In this research, a method is presented to create multivariate ...
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Purpose: Pair formation is an important step in pair trading that has only been examined manually or through numerical instructions. These methods fail in the multivariate mode and do not consider conflicting goals in the problem structure. In this research, a method is presented to create multivariate pair combinations by considering contradictory multiple goals in stock pair trading.
Methodology: In this study, the statistical sample is limited to the top 30 companies listed on the Tehran Stock Exchange due to the need for high-frequency transactions. The problem is developed in the form of a mixed integer programming model (MIP), and due to non-convex constraints and exponential solution space, a multi-objective genetic algorithm is used to obtain multivariate pair combinations. To achieve multiple goals, the developed type of genetic algorithm, namely, The Chaotic Non-dominated Sorting Genetic Algorithm (CNSGA-II), was used. In this method, chaos theory is used to create the initial population of the genetic algorithm in order to obtain appropriate and high-precision solutions.
Findings: The results showed that the use of chaos theory could increase the degree of convergence in evolutionary algorithms. In addition, these results indicate the superiority of the multi-objective pair trading strategy based on the distance approach over the traditional single-objective model.
Originality/Value: In order to optimize pair trading, the Non-dominated Sorting Genetic Algorithm was used. Also, the initial population of individuals was created in a multi-objective genetic algorithm based on chaos theory.
meta-heuristic algorithms
Sajad Janbaz; Seyed Mohammadreza Davoodi; Abdolmajid Abdolbaghi Ataabadi
Abstract
Purpose: The current research aims to present a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using Non-dominated Sorting Genetic Algorithm II (NSGA-II).Methodology: This research presents a multi-objective mathematical model integrating ...
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Purpose: The current research aims to present a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using Non-dominated Sorting Genetic Algorithm II (NSGA-II).Methodology: This research presents a multi-objective mathematical model integrating scheduling and financial flow optimization in civil engineering projects. This research addresses the scheduling and financial flow challenges in construction companies' production projects. The objective is to develop a multi-objective mathematical model that integrates scheduling and financial considerations to optimize resource allocation and minimize costs. The statistical population is in the form of a case study, and the required information and data were collected through interviews with managers of Kisson Construction Company.Findings: NSGA-II was used as an optimization algorithm to find efficient multi-objective solutions, and optimal results were presented to select civil and construction projects.Originality/Value: This research contributes to the field by proposing a novel multi-objective mathematical model that integrates scheduling and financial flow considerations in production projects. The use of the NSGA-II algorithm enhances the efficiency of finding optimal solutions. The findings can be valuable for decision-making when selecting construction and production projects.
meta-heuristic algorithms
Ebrahim Farbod; Alireza Hamidieh
Abstract
Purpose: The purpose of this study is to explain the impact of green supply chain on economic performance, emphasizing the mediating role of green innovation, environmental management and quality management in companies listed on the Tehran Stock Exchange.Methodology: In this research, the multivariate ...
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Purpose: The purpose of this study is to explain the impact of green supply chain on economic performance, emphasizing the mediating role of green innovation, environmental management and quality management in companies listed on the Tehran Stock Exchange.Methodology: In this research, the multivariate perceptron neural network approach and modeling of variance-based structural equations in SPSS26 and SmartPls3.3.3 software have been investigated.Findings: The results show that green supply chain management affects economic performance and increases green innovation, environmental management and quality management of economic performance. The establishment of a green supply chain has led to the observance of environmental requirements, and by observing environmental requirements, labor productivity is improved through specialized training of employees.Originality/Value: In previous studies have not considered the pros and cons of the relationship between environmental management and labor productivity. In this study, according to the selection of companies listed on the Tehran Stock Exchange during the covid-19 period and the statistical sample selected by systematic elimination method and available sampling, these views were examined. Also, in the research method, most researches have only fitted the model with structural equations and regression equations, while in this research, the proposed model fits with multilayer perceptron neural network method and variance-based structural equations and finally to evaluate the performance prediction comparison model. Economically, the root mean square error index is used
meta-heuristic algorithms
Hojatollah Rajabi Moshtaghi; Abbass Toloie-Eshlaghy; Mohammad Reza Motadel
Abstract
Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world ...
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Purpose: In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive; that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization Algorithm (MOA). Methodology: Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm.Findings: The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping Algorithm, and Imperialist Competitive Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, and Grasshopper Optimization Algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time". The results show the excellent performance of the proposed algorithm.Originality/Value: In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".
meta-heuristic algorithms
Mohammadreza Etebari; Naser Feghhi Farahmand; Soleyman Iranzadeh
Abstract
Purpose: Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The purpose ...
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Purpose: Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The purpose of this paper is to compile a credit forecasting model for legal customers of private banks by using meta-heuristic algorithms in the branches of Pasargad Bank in the northwest of Iran.Methodology: This research is base on the purpose of developmental research and based on the method of performing descriptive work. The statistical population of this study is in two sections of banking experts and legal customers of Pasargad Bank in the northwest of the Iran. The statistical sample size for the first community of 58 banking experts including managers, credit officials and heads of branches in with credit work experience in private banks and for the second community, 427 legal clients were selected based on targeted sampling. In order to collect data in this research, a questionnaire and documents of Pasargad Bank have been used. The validity of the questionnaire was investigated as content validity and based on the indicators of content validity ratio and content validity index. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient. In order to analyze the research data, t-test, confirmatory factor analysis, multilayer neural network, genetically trained neural network, trained neural network with particle swarm optimization and trained neural network with differential evolution will be used.Findings: The research findings show that all four models are able to predict the credit predictions of the legal customers of private banks and the best way to predict the credit predictions of the legal customers of private banks is the neural network trained with differential evolution algorithm with the least amount of error compared to the other three methods.Originality/Value: In this research by using meta-heuristic algorithms, a new credit forecasting model produce for legal customers of private banks with the least amount of error.
meta-heuristic algorithms
Sasmiran Khaje zadeh; Shadi Shahverdiani; amir Daneshvar; Mahdi Madanchi zaj
Abstract
One of the most attractive areas for decision-making in the face of uncertainty is optimal stock portfolio. In decision making for investment, two factors are very important and are the basis of investment. These two factors are risk and return, and in this regard, the study and study of investors to ...
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One of the most attractive areas for decision-making in the face of uncertainty is optimal stock portfolio. In decision making for investment, two factors are very important and are the basis of investment. These two factors are risk and return, and in this regard, the study and study of investors to select the best investment portfolio is done according to the amount of risk and its return. A portfolio is a combination of assets formed by an investor to invest. The process of selecting a stock portfolio is one of the issues that have been the focus of many researchers. The aim of this study is to create an optimal stock portfolio using the predicted data. The statistical sample of the research includes the financial data of Iranian stock exchange companies during the years 1390 to 1397. In this study, using stock regression algorithm to predict stock returns, and finally using Markov method and spectral clustering algorithm, the necessary filter to select the appropriate initial data was performed and cultural meta-processing method with prediction data, It provided the optimal portfolio of stocks for the investor group with risk-taking as well as risk-averse. The research results show that the cultural transcendental algorithm, according to Sharp's method, has the ability to create an optimal stock portfolio using predicted data using the Marquis method for venture capitalists and risk averse investors.
meta-heuristic algorithms
javid ghahremani nahr
Abstract
With the expansion and intensification of competition, supply chain management has become one of the key issues facing economic firms, as all the activities of organizations to produce products, improve quality, reduce costs and provide services required by customers, has been affected. In this research, ...
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With the expansion and intensification of competition, supply chain management has become one of the key issues facing economic firms, as all the activities of organizations to produce products, improve quality, reduce costs and provide services required by customers, has been affected. In this research, a closed loop supply chain network include levels of (manufacturing centers, demand zones, collection centers and disposal centers) under certainty is considered. The main objective of this paper is to determine the optimal number and location of potential facilities and determine the optimal flow considering the minimization total supply chain network cost. To solve this model, a new metaheuristics algorithm called the whale Optimization algorithm has been used with novel priority-based encoding. Also, to demonstrate the high efficiency of the proposed method, 21 sample problems were designed in small, medium and large sizes, and the results obtained from the solving method and the results obtained from the methodology for solving the subject literature were compared. Comparisons between solving methods with consideration of the two averages of the objective functions and the average computational time indicate the efficiency of the proposed solution method for the comparison of the other methods of solving.
meta-heuristic algorithms
Forough Shahabi; Fereshte Pourahangarian; Homayoon Beheshti
Abstract
One of the fundamental problems in image processing is image segmentation identifying the objects and other structures in the image. Image thresholding is one of the widely used methods for image segmentation that can separate pixels based on the specified thresholds. The Otsu method calculates the thresholds ...
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One of the fundamental problems in image processing is image segmentation identifying the objects and other structures in the image. Image thresholding is one of the widely used methods for image segmentation that can separate pixels based on the specified thresholds. The Otsu method calculates the thresholds to divide two or multiple classes. Classes are based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds surges the computational time of the segmentation. To overcome this drawback, the combination of Otsu and the evolutionary algorithm is often effective. In this paper, we present a hybrid method utilizing the CSA and Otsu for multilevel thresholding. The result of our method has been compared with the three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. The evaluation consequence of the five benchmark images shows time and uniformity criteria have been improved.
meta-heuristic algorithms
Ehsan Aghdaee; Ali Husseinzadeh Kashan
Abstract
In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of ...
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In managing a project, reliable prediction is an essential element for success. Project managers are always looking for controlling their projects to make sure the the project is within acceptable limits. For a long time, the earned value management (EVM) for pursuing time performance and the cost of the project has been used. However, using this method to valuate project time performance by utilizing the time performance index (SPI) by researchers and practitioners has been faced with serious criticism. Therefore, the present study proposes a framework for assessment and prediction of the temporal performance of each of the thread activities in project management. In this framework, using the multi objective league championship algorithm (MOLCA), the initial plan of the projects is optimized and then via using the Kalman Filter prediction method, project execution planning is done such that the projects in conditions of uncertainty could be forecasted and ahead horizon being demonstrated accurately with the least error for project managers. In this paper, in order to ensure the quality of the solutions, the output of the algorithm is compared with genetic algorithms (NSGII) and particle swarm optimization (MOPSO), where results demonstrate the superiority of the proposed algorithm.
meta-heuristic algorithms
Vida Karbasi bonab; Mahdi Yousefi Nejad Attari; Ensiyeh Neishabouri
Abstract
Vendor managed inventory (VMI) is one of the popular strategies to manage inventory control system, in this strategy, the vendor is responsible for controlling and replenishment the inventory of retailers. In this paper, a bi-objective vendor managed inventory (BOVMI) model with fuzzy demand was investigated ...
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Vendor managed inventory (VMI) is one of the popular strategies to manage inventory control system, in this strategy, the vendor is responsible for controlling and replenishment the inventory of retailers. In this paper, a bi-objective vendor managed inventory (BOVMI) model with fuzzy demand was investigated for a supply chain problem with multiple vendors and retailers, the fuzzy demand is formulated using trapezoidal fuzzy number (TrFN) where the centroid defuzzification method is employed to defuzzify fuzzy output functions. The vendor confronts two constraints: number of orders and available budget and minimizing the total inventory cost and optimizing the warehouse space are the two objectives of the model. Since the proposed model is formulated ino a bi-objective integer nonlinear programming (INLP) problem, an non-dominated Sorting genetic algorithm-II (NSGA-II) has been developed to find Pareto front solution. To improve the performance of algorithm has been calibrated using Taguchi method. Finally, conclusions are made and future research works are recommended.