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Soft Computing

, Volume 15, Issue 11, pp 2127–2140 | Cite as

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

  • Hui Wang
  • Zhijian Wu
  • Shahryar Rahnamayan
Focus

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.

Keywords

Differential evolution Opposition-based DE Evolutionary computation Global optimization High-dimensional optimization Large-scale optimization 

Notes

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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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina
  2. 2.Faculty of Engineering and Applied ScienceUniversity of Ontario Institute of Technology (UOIT)OshawaCanada

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