An adaptive differential evolution algorithm based on adaptive evolution strategy and diversity enhancement
Differential Evolution (DE) is a widely used and highly effective heuristic global optimization algorithm, but it often stagnates in the later stages of evolution due to a sudden drop in population diversity. To address these challenges, we propose an Adaptive Differential Evolution algorithm based...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025026209 |
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| Summary: | Differential Evolution (DE) is a widely used and highly effective heuristic global optimization algorithm, but it often stagnates in the later stages of evolution due to a sudden drop in population diversity. To address these challenges, we propose an Adaptive Differential Evolution algorithm based on Adaptive Evolution Strategy and Diversity Enhancement (ADE-AESDE). First, we introduce a multi-stage mutation strategy controlled by an adaptive stagnation index, which rapidly rotates the mutation strategy based on the number of times an individual stagnates. Second, we introduce a novel individual ranking factor that divides the generation of the scaling factor into three distinct phases, thereby optimizing balanced exploration and exploitation throughout the evolutionary process. Third, we propose a stagnation detection mechanism based on population hypervolume, combining guided differential jump, seed-pool recombination, and archive-guided differential replay strategies to update stagnant individuals and enhance population diversity. Finally, we validate our algorithm on multiple benchmarks of varying dimensionalities from the CEC 2013, 2014, and 2017 test suites as well as on real-world application problems, and all results undergo rigorous statistical significance testing. Compared to many state-of-the-art DE variants and heuristic algorithms, ADE-AESDE demonstrates strong competitiveness in optimization performance, convergence and diversity. |
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| ISSN: | 2590-1230 |