期刊
SWARM AND EVOLUTIONARY COMPUTATION
卷 45, 期 -, 页码 1-14出版社
ELSEVIER
DOI: 10.1016/j.swevo.2018.12.006
关键词
Differential evolution; Top-bottom strategy; Failure remember strategy; Self-adaptive parameter
资金
- National Natural Science Foundation of China [61375066, 71772060, 11671052]
Differential evolution (DE) has attracted more and more attention. However, the neighborhood and direction information has not been fully utilized in exploration and exploitation stages. A failure remember-driven self-adaptive differential evolution algorithm, ATBDE, is proposed in this paper, which uses Top-Bottom strategy with optional archive and a parameter self-adapting strategy driven by Failure Remember operation. Top-Bottom strategy utilizes historical heuristic information obtained from the successful and failed individuals, respectively, to guide individuals toward the potential more promising regions in an optional archive manner. This strategy is also theoretically analyzed for the best implementation. The failure remember-driven parameter adaption strategy shares the positive search experience from the successful individuals and abandons the negative search experience for those successive failing individuals. Comprehensive experiments show that ATBDE is better than, or at least comparable to, other DE algorithms in terms of convergence performance and accuracy.
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