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• W2093722713 abstract "Differential evolution (DE), like other probabilistic optimisation algorithms, sometimes exhibits premature convergence and stagnation. It is analysed by researchers that the DE is better in exploration of the search space compared to exploitation. In the solution search process of DE, there is enough chance to skip the true solution due to large step size. In order to balance the exploration and exploitation capability of the DE, a power law-based local search strategy is proposed and integrated with DE. In the proposed strategy, new solutions are generated around the best solution and it helps to enhance the exploitation capability of DE. The experiments on 14 un-biased test problems of different complexities show that the proposed strategy outperforms the basic DE and recent variants of DE namely, self-adaptive DE (SaDE) and scale factor local search DE (SFLSDE) in most of the experiments." @default.
• W2093722713 created "2016-06-24" @default.
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• W2093722713 date "2013-01-01" @default.
• W2093722713 modified "2023-09-26" @default.
• W2093722713 title "Power law-based local search in differential evolution" @default.
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• W2093722713 doi "https://doi.org/10.1504/ijcistudies.2013.055217" @default.
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