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

Authors:
Hui Wang;Zhijian Wu;Shahryar Rahnamayan
Affiliations:
Wuhan University, State Key Laboratory of Software Engineering, 430072, Wuhan, China;Wuhan University, State Key Laboratory of Software Engineering, 430072, Wuhan, China;University of Ontario Institute of Technology (UOIT), Faculty of Engineering and Applied Science, 2000 Simcoe Street North, L1H 7K4, Oshawa, ON, Canada
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Year:
2011

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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.