Optimization plays an effective role in various disciplines of science and engineering.Optimization problems should either be optimized using the appropriate method(i.e.,minimization or maximization).Optimization algo...Optimization plays an effective role in various disciplines of science and engineering.Optimization problems should either be optimized using the appropriate method(i.e.,minimization or maximization).Optimization algorithms are one of the efficient and effective methods in providing quasioptimal solutions for these type of problems.In this study,a new algorithm called the Mutated Leader Algorithm(MLA)is presented.The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader.In addition to information about the best member of the population,themutated leader also contains information about the worst member of the population,as well as other normal members of the population.The proposed MLA is mathematically modeled for implementation on optimization problems.A standard set consisting of twenty-three objective functions of different types of unimodal,fixed-dimensional multimodal,and high-dimensional multimodal is used to evaluate the ability of the proposed algorithm in optimization.Also,the results obtained from theMLA are compared with eight well-known algorithms.The results of optimization of objective functions show that the proposed MLA has a high ability to solve various optimization problems.Also,the analysis and comparison of the performance of the proposed MLA against the eight compared algorithms indicates the superiority of the proposed algorithm and ability to provide more suitable quasi-optimal solutions.展开更多
There are many optimization problems in different branches of science that should be solved using an appropriate methodology.Populationbased optimization algorithms are one of the most efficient approaches to solve th...There are many optimization problems in different branches of science that should be solved using an appropriate methodology.Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems.In this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization problems.The main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population matrix.Therefore,in AMBO,any member of the population can play a role in updating the population matrix.The theory of AMBO is described and then mathematically modeled for implementation on optimization problems.The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions.In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also implemented.The optimization results demonstrate the ability of AMBO to solve various optimization problems.Also,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.展开更多
基金PT(corresponding author)was supported by the Excellence project PrF UHK No.2202/2020-2022 and Long-term development plan of UHK for year 2021,University of Hradec Králové,Czech Republic,https://www.uhk.cz/en/faculty-of-science/about-faculty/offic ial-board/internal-regulations-and-governing-acts/governing-acts/deans-decision/2020#grant-competi tion-of-fos-uhk-excellence-for-2020.
文摘Optimization plays an effective role in various disciplines of science and engineering.Optimization problems should either be optimized using the appropriate method(i.e.,minimization or maximization).Optimization algorithms are one of the efficient and effective methods in providing quasioptimal solutions for these type of problems.In this study,a new algorithm called the Mutated Leader Algorithm(MLA)is presented.The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader.In addition to information about the best member of the population,themutated leader also contains information about the worst member of the population,as well as other normal members of the population.The proposed MLA is mathematically modeled for implementation on optimization problems.A standard set consisting of twenty-three objective functions of different types of unimodal,fixed-dimensional multimodal,and high-dimensional multimodal is used to evaluate the ability of the proposed algorithm in optimization.Also,the results obtained from theMLA are compared with eight well-known algorithms.The results of optimization of objective functions show that the proposed MLA has a high ability to solve various optimization problems.Also,the analysis and comparison of the performance of the proposed MLA against the eight compared algorithms indicates the superiority of the proposed algorithm and ability to provide more suitable quasi-optimal solutions.
基金PT(corresponding author)was supported by the Excelence project PˇrF UHK No.2202/2020-2022 and Long-term development plan of UHK for year 2021,University of Hradec Králové,Czech Republic,https://www.uhk.cz/en/faculty-of-science/about-faculty/officialboard/internal-regulations-and-governing-acts/governing-acts/deans-decision/2020#grant-competition-of-fos-uhk-excellence-for-2020.
文摘There are many optimization problems in different branches of science that should be solved using an appropriate methodology.Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems.In this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization problems.The main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population matrix.Therefore,in AMBO,any member of the population can play a role in updating the population matrix.The theory of AMBO is described and then mathematically modeled for implementation on optimization problems.The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions.In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also implemented.The optimization results demonstrate the ability of AMBO to solve various optimization problems.Also,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.
