Crowding clustering genetic algorithm for multimodal function optimization

Authors:
Ling Qing;Wu Gang;Yang Zaiyue;Wang Qiuping
Affiliations:
Department of Automation, University of Science and Technology of China, China;Department of Automation, University of Science and Technology of China, China;Department of Mechanical Engineering, University of Hong Kong, Hong Kong, China;National Synchrotron Radiation Laboratory, University of Science and Technology of China, China
Venue:
Applied Soft Computing
Year:
2008

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Abstract

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches and clustering operation to eliminate genetic drift. Numerical experiments on standard test functions indicate that crowding clustering genetic algorithm is superior to both standard crowding and deterministic crowding in quantity, quality and precision of multi-optimum search. The proposed algorithm is applied to the practical optimal design of varied-line-spacing holographic grating and achieves satisfactory results.