An improved differential evolution and novel crowding distance metric for multi-objective optimization

摘要：

In this paper, an improved differential evolution based on hill-climbing techniques is proposed for multi-objective optimization. Multi-objective differential evolution optimizers are often trapped in local optima and converge slowly. A simple hillclimbing is employed to keep the diversity of population and escape from local optima. A novel crowding-distance computation procedure is proposed in order that the solutions in the neighborhood of the solutions with smallest and largest function values or locating in a lesser crowded region will have higher probability to be preserved. The proposed algorithm is tested on several classical MOP benchmark functions. The simulation results show that the proposed algorithm can obtain the solutions to be widely spread on the true Pareto optimal front.

关键词： Differential evolution Crowding distance Hill climbing Multi-objective optimization Pareto front

作者: Chengfu Sun

作者单位: Computer Engineering College HuaiYin Institute of Technology Huaian, Jiangsu Province, China

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)

会议地点: 武汉

会议语种:英文

页码: 265-268

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）