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Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

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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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Acknowledgments

The authors would like to thank Dr. D. Molina for his suggestions in the implementation of DE. The authors also thank the editor and the anonymous reviewers for their detailed and constructive comments that helped us to increase the quality of this work. This work was supported by the National Natural Science Foundation of China (No.: 61070008), and the Jiangxi Province Science & Technology Pillar Program (No.: 2009BHB16400).

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Hui Wang & Zhijian Wu

  2. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canada

    Shahryar Rahnamayan

Authors
  1. Hui Wang
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  2. Zhijian Wu
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  3. Shahryar Rahnamayan
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Correspondence to Zhijian Wu.

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Wang, H., Wu, Z. & Rahnamayan, S. Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems. Soft Comput 15, 2127–2140 (2011). https://doi.org/10.1007/s00500-010-0642-7

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