Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems |
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Authors: | Hui Wang Zhijian Wu Shahryar Rahnamayan |
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Affiliation: | (1) State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China;(2) Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canada |
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Abstract: | This paper presents a novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of
differential evolution (DE) to solve high-dimensional optimization problems efficiently. The proposed approach, namely GODE,
employs similar schemes of opposition-based DE (ODE) for opposition-based population initialization and generation jumping
with GOBL. Experiments are conducted to verify the performance of GODE on 19 high-dimensional problems with D = 50, 100, 200, 500, 1,000. The results confirm that GODE outperforms classical DE, real-coded CHC (crossgenerational elitist
selection, heterogeneous recombination, and cataclysmic mutation) and G-CMA-ES (restart covariant matrix evolutionary strategy)
on the majority of test problems. |
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Keywords: | |
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