Multiple individual guided differential evolution with time varying and feedback information-based control parameters
Differential evolution (DE) is a simple and efficient metaheuristic algorithm for solving global optimization problems. It is used widely in various fields due to its concise structure and strong search ability. In this study, an extended version of the DE named multiple individual guided differenti...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/166993 |
| _version_ | 1811685377417674752 |
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| author | Gupta, Shubham Su, Rong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Gupta, Shubham Su, Rong |
| author_sort | Gupta, Shubham |
| collection | NTU |
| description | Differential evolution (DE) is a simple and efficient metaheuristic algorithm for solving global optimization problems. It is used widely in various fields due to its concise structure and strong search ability. In this study, an extended version of the DE named multiple individual guided differential evolution (MGDE) is proposed. The MGDE is distinguished by introducing a novel mutation strategy based on multiple guiding individuals of the DE population to manage the diversity and convergence. The base vector of the mutation strategy is defined as a center of guiding individuals and the difference vectors are assigned to perform a search towards one of the top fitted and top diversified individuals available in the population. The control parameters of the DE are adjusted in a way to provide a suitable transition from exploration to exploitation and to utilize the information of recent success history of evolution. The performance of the proposed MGDE is evaluated on three different benchmark sets including IEEE CEC2014, IEEE CEC2017, and IEEE CEC2011 of real-world problems. Different performance metrics such as average and standard deviation of fitness, ranking of algorithms, Wilcoxon signed-rank test, and convergence analysis are used to analyze and compare the results of the MGDE with several other metaheuristic algorithms. Comparison attest that the proposed MGDE algorithm is highly competitive with the other metaheuristic algorithms. |
| first_indexed | 2024-10-01T04:43:33Z |
| format | Journal Article |
| id | ntu-10356/166993 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:43:33Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1669932023-05-12T15:40:44Z Multiple individual guided differential evolution with time varying and feedback information-based control parameters Gupta, Shubham Su, Rong School of Electrical and Electronic Engineering Science::Mathematics::Applied mathematics::Optimization Metaheuristics Differential Evolution Differential evolution (DE) is a simple and efficient metaheuristic algorithm for solving global optimization problems. It is used widely in various fields due to its concise structure and strong search ability. In this study, an extended version of the DE named multiple individual guided differential evolution (MGDE) is proposed. The MGDE is distinguished by introducing a novel mutation strategy based on multiple guiding individuals of the DE population to manage the diversity and convergence. The base vector of the mutation strategy is defined as a center of guiding individuals and the difference vectors are assigned to perform a search towards one of the top fitted and top diversified individuals available in the population. The control parameters of the DE are adjusted in a way to provide a suitable transition from exploration to exploitation and to utilize the information of recent success history of evolution. The performance of the proposed MGDE is evaluated on three different benchmark sets including IEEE CEC2014, IEEE CEC2017, and IEEE CEC2011 of real-world problems. Different performance metrics such as average and standard deviation of fitness, ranking of algorithms, Wilcoxon signed-rank test, and convergence analysis are used to analyze and compare the results of the MGDE with several other metaheuristic algorithms. Comparison attest that the proposed MGDE algorithm is highly competitive with the other metaheuristic algorithms. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This research is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - PrePositioning (IAF-PP) (Award A19D6a0053). 2023-05-10T06:40:18Z 2023-05-10T06:40:18Z 2023 Journal Article Gupta, S. & Su, R. (2023). Multiple individual guided differential evolution with time varying and feedback information-based control parameters. Knowledge-Based Systems, 259, 110091-. https://dx.doi.org/10.1016/j.knosys.2022.110091 0950-7051 https://hdl.handle.net/10356/166993 10.1016/j.knosys.2022.110091 2-s2.0-85145612579 259 110091 en A19D6a0053 Knowledge-Based Systems © 2022 Elsevier B.V. All rights reserved. This paper was published in Knowledge-Based Systems and is made available with permission of Elsevier B.V. application/pdf |
| spellingShingle | Science::Mathematics::Applied mathematics::Optimization Metaheuristics Differential Evolution Gupta, Shubham Su, Rong Multiple individual guided differential evolution with time varying and feedback information-based control parameters |
| title | Multiple individual guided differential evolution with time varying and feedback information-based control parameters |
| title_full | Multiple individual guided differential evolution with time varying and feedback information-based control parameters |
| title_fullStr | Multiple individual guided differential evolution with time varying and feedback information-based control parameters |
| title_full_unstemmed | Multiple individual guided differential evolution with time varying and feedback information-based control parameters |
| title_short | Multiple individual guided differential evolution with time varying and feedback information-based control parameters |
| title_sort | multiple individual guided differential evolution with time varying and feedback information based control parameters |
| topic | Science::Mathematics::Applied mathematics::Optimization Metaheuristics Differential Evolution |
| url | https://hdl.handle.net/10356/166993 |
| work_keys_str_mv | AT guptashubham multipleindividualguideddifferentialevolutionwithtimevaryingandfeedbackinformationbasedcontrolparameters AT surong multipleindividualguideddifferentialevolutionwithtimevaryingandfeedbackinformationbasedcontrolparameters |