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• W2070900588 abstract "Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors." @default.
• W2070900588 created "2016-06-24" @default.
• W2070900588 creator A5000809799 @default.
• W2070900588 creator A5042483034 @default.
• W2070900588 date "2015-02-01" @default.
• W2070900588 modified "2023-09-24" @default.
• W2070900588 title "Self-adaptive differential evolution algorithm with discrete mutation control parameters" @default.
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• W2070900588 doi "https://doi.org/10.1016/j.eswa.2014.09.046" @default.
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