Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems

Shang-Kuan Chen, Gen-Han Wu () and Yu-Hsuan Wu
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Shang-Kuan Chen: Department of Computer Science & Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
Gen-Han Wu: Department of Industrial Engineering & Engineering, Yuan Ze University, Taoyuan 32003, Taiwan
Yu-Hsuan Wu: Department of Industrial Engineering & Engineering, Yuan Ze University, Taoyuan 32003, Taiwan

Mathematics, 2025, vol. 13, issue 10, 1-20

Abstract: 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.

Keywords: differential evolution; particle swarm optimization; self-adaptive parameters; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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