哈尔滨工程大学学报2011，Vol.32Issue(2)：223-227,5.DOI:10.3969/j.issn.1006-7043.2011.02.015
多模态函数优化的拥挤差分进化算法
Multimodal function optimization using a crowding differential evolution
毕晓君 1王义新1
作者信息
- 1. 哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨,150001
- 折叠
摘要
Abstract
This paper presents a crowding differential evolution(CDE) algorithm applied to multimodal function optimization for finding all the extreme solutions, using DEs( differential evolution, DE) global search strategy and internal parallel pattern. The high crowding factor(CF) value search avoids the replacement error, maintains diversity of species, and can accurately locate all the multimodal functions optimal solutions and extreme solutions. Meanwhile, this algorithm has a lot of advantages such as less parameters, simple operator and swift convergence rate.The algorithm is compared with crowding genetic algorithm and simulation experiment results show that crowding differential evolution is better than crowding genetic algorithm (CGA) in both convergence rate and convergence accuracy.关键词
多模态函数优化/拥挤模型/差分进化算法/群集因子Key words
multimodal function optimization/ crowding model/ differential evolution/ crowding factor分类
信息技术与安全科学引用本文复制引用
毕晓君,王义新..多模态函数优化的拥挤差分进化算法[J].哈尔滨工程大学学报,2011,32(2):223-227,5.
