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- W2905807087 abstract "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." @default.
- W2905807087 created "2019-01-01" @default.
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- W2905807087 date "2019-03-01" @default.
- W2905807087 modified "2023-10-18" @default.
- W2905807087 title "A failure remember-driven self-adaptive differential evolution with top-bottom strategy" @default.
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- W2905807087 doi "https://doi.org/10.1016/j.swevo.2018.12.006" @default.
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