Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems
In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/10/1647 |
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| author | Shang-Kuan Chen Gen-Han Wu Yu-Hsuan Wu |
| author_facet | Shang-Kuan Chen Gen-Han Wu Yu-Hsuan Wu |
| author_sort | Shang-Kuan Chen |
| collection | DOAJ |
| description | In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indicate that MBDE2 and IHDE-BPSO3 outperform the original differential evolution algorithm and its extended variants, consistently achieving optimal solutions in most cases. The findings suggest that the proposed improved differential evolution algorithm is highly adaptable across various problems, yielding superior results. Additionally, integrating memory properties significantly enhances the algorithm’s performance and effectiveness. |
| format | Article |
| id | doaj-art-bceaa66920a5464aa67f02a3dbcdbb91 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-bceaa66920a5464aa67f02a3dbcdbb912025-08-20T03:14:36ZengMDPI AGMathematics2227-73902025-05-011310164710.3390/math13101647Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization ProblemsShang-Kuan Chen0Gen-Han Wu1Yu-Hsuan Wu2Department of Computer Science & Engineering, Yuan Ze University, Taoyuan 32003, TaiwanDepartment of Industrial Engineering & Engineering, Yuan Ze University, Taoyuan 32003, TaiwanDepartment of Industrial Engineering & Engineering, Yuan Ze University, Taoyuan 32003, TaiwanIn this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indicate that MBDE2 and IHDE-BPSO3 outperform the original differential evolution algorithm and its extended variants, consistently achieving optimal solutions in most cases. The findings suggest that the proposed improved differential evolution algorithm is highly adaptable across various problems, yielding superior results. Additionally, integrating memory properties significantly enhances the algorithm’s performance and effectiveness.https://www.mdpi.com/2227-7390/13/10/1647differential evolutionparticle swarm optimizationself-adaptive parametersoptimization |
| spellingShingle | Shang-Kuan Chen Gen-Han Wu Yu-Hsuan Wu Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems Mathematics differential evolution particle swarm optimization self-adaptive parameters optimization |
| title | Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems |
| title_full | Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems |
| title_fullStr | Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems |
| title_full_unstemmed | Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems |
| title_short | Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems |
| title_sort | memory based differential evolution algorithms with self adaptive parameters for optimization problems |
| topic | differential evolution particle swarm optimization self-adaptive parameters optimization |
| url | https://www.mdpi.com/2227-7390/13/10/1647 |
| work_keys_str_mv | AT shangkuanchen memorybaseddifferentialevolutionalgorithmswithselfadaptiveparametersforoptimizationproblems AT genhanwu memorybaseddifferentialevolutionalgorithmswithselfadaptiveparametersforoptimizationproblems AT yuhsuanwu memorybaseddifferentialevolutionalgorithmswithselfadaptiveparametersforoptimizationproblems |