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• W2224582445 abstract "The performance of differential evolution (DE) is significantly influenced by the choice of crossover strategies; therefore, a self-adaptive differential evolution algorithm with crossover strategies adaptation (CSA-SADE) is proposed in this paper to enhance the performance of DE. In CSA-SADE, the suitable control parameters, mutation strategies, and crossover strategies can be achieved in different evolution stages. To demonstrate the effectiveness of CSA-SADE, the proposed algorithm is compared with eight state-of-the-art evolutionary algorithms. The simulation results indicate that CSA-SADE outperforms five improved DE algorithms and three non-DE approaches on a set of 25 CEC2005 benchmark functions. Additionally, the proposed algorithm is employed to estimate the kinetic parameters of mercury oxidation; the results show that CSA-SADE performs better than the compared algorithms in this simulation example." @default.
• W2224582445 created "2016-06-24" @default.
• W2224582445 creator A5042483034 @default.
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• W2224582445 date "2016-02-01" @default.
• W2224582445 modified "2023-10-16" @default.
• W2224582445 title "Self-adaptive differential evolution algorithm with crossover strategies adaptation and its application in parameter estimation" @default.
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• W2224582445 doi "https://doi.org/10.1016/j.chemolab.2015.12.020" @default.
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