An Evolutionary Algorithm for Constrained Multi-objective Optimization Problems

摘要：

Constrained multi-objective optimization problems (CMOP) are challenging and difficult to solve. In this paper, a simple and practical evolutionary algorithm for constrained multi-objective optimization problems (EACMOP) is presented, by defining constraints using non-parameter punitive Junctions, using Pareto strength value to represent Pareto order strength among individuals and using crowding density to ensure group diversity. It defines the evolutionary algorithm fitness Junctions by combining constraint treatment, comparison of Pareto strength optimization and crowding density. Test results on several benchmark Junctions showed that the approach is effective and robust.

关键词： Evolutionary algorithms multi-objective optimization constrained

作者: Hua-Qing Min Yu-Ren Zhou Yan-Sheng Lu Jia-zhi Jiang

作者单位: College of Computer Science & Engineering, HuaZhong University of Science and Technology, Wuhan 4300 School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, College of Computer Science & Engineering, HuaZhong University of Science and Technology, Wuhan 4300

会议类型: 国际会议

会议名称: 2006 Asia-Pacific Services Computing Conference(IEEE亚太地区服务计算会议)

会议地点: 广州

会议语种:英文

页码: 667-670

在线出版日期: 2006-12-12（万方平台首次上网日期，不代表论文的发表时间）