Crowding clustering genetic algorithm for multimodal function optimization

作     者：Ling, Qing Wu, Gang Yang, Zaiyue Wang, Qiuping 

作者机构：Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Univ Sci & Technol China Natl Synchrotron Radiat Lab Hefei 230026 Peoples R China 

出 版 物：《APPLIED SOFT COMPUTING》 (应用软计算)

年 卷 期：2008年第8卷第1期

页      面：88-95页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：multimodal function optimization crowding clustering genetic algorithm evolutionary computation genetic drift varied line-spacing holographic grating 

摘      要：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. (c) 2006 Elsevier B.V. All rights reserved.