An Adaptive Niching-Based Evolutionary Algorithm for Optimizing Multi-Modal Function

作     者：Luo, Jie Gu, Fangqing 

作者机构：S China Normal Univ Sch Phys & Telecommun Engn Guangzhou Guangdong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE》 (国际图形识别与人工智能杂志)

年 卷 期：2016年第30卷第3期

页      面：1659007-1659007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：Evolutionary algorithm multi-modal optimization problems niching decomposition 

摘      要：This paper presents a niching-based evolutionary algorithm for optimizing multi-modal optimization function. Provided that the potential optima are characterized by a relatively smaller objective value than their neighbors and by a relatively large distance from points with smaller objective values, we identify potential optima from individuals. Using them as seeds, a population is decomposed into a number of subpopulations without introducing new parameters. Moreover, we present an adaptive allocating strategy of assigning different computational resources to different subpopulations upon the fact that discovering different optima may have different computational difficulty. The proposed method is compared with three state-of-the-art multi-modal optimization approaches on a benchmark function set. The extensive experimental results demonstrate its efficacy.