Improving Differential Evolution Using Hybrid Strategies for Multimodal Optimization

摘要：

Differential Evolution (DE) is a new evolutionary optimization technique, which has shown good performance in many optimization problems. However, DE as well as other evolutionary algorithms easily falls into local minima in solving complex multimodal problems. In this paper, we propose an improved DE variant by employing hybrid strategies for solving multimodal problems. The proposed approach is called HDE, which utilizes oppositionbased learning concept and optional external archive. Experiments on a multimodal benchmark set show that HDE achieves good solutions in many test cases.

关键词： differential evolution evolutionary computation multimodal optimization global optimization

作者: Xiaoting Ma Chen Chen

作者单位: School of Information Engineering Lanzhou University of Finance and Economics Lanzhou 730020, China Modern Education Technology and Information Center Lanzhou University of Finance and Economics Lanzh

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Automation Engineering(ICCAE 2011)(2011年第三届IEEE计算机与自动化工程国际会议)

会议地点: 重庆

会议语种:英文

页码: 301-304

在线出版日期: 2011-01-21（万方平台首次上网日期，不代表论文的发表时间）