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• W2811147219 endingPage "1959004" @default.
• W2811147219 startingPage "1959004" @default.
• W2811147219 abstract "Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-population evolutionary algorithm adapting to complementary evolutionary strategy (DPCEDT) based on the study of differential evolution algorithm, teaching and learning-based optimization algorithm. The simulation results show that the algorithm performs better than the TLBO-DE, HDT and DPDT and some other algorithms do in most test functions. It suggests that the complementary evolutionary strategies are more advantageous than other evolutionary strategies in dual-population evolutionary algorithms." @default.
• W2811147219 created "2018-07-10" @default.
• W2811147219 creator A5000432967 @default.
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• W2811147219 date "2018-10-11" @default.
• W2811147219 modified "2023-10-12" @default.
• W2811147219 title "A Dual-Population Evolutionary Algorithm Adapting to Complementary Evolutionary Strategy" @default.
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• W2811147219 doi "https://doi.org/10.1142/s0218001419590043" @default.
• W2811147219 hasPublicationYear "2018" @default.
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