A Novel Diversity Preservation Strategy based on Ranking Integration for S lving Some Specific Multi-Objective Problems

摘要：

recent decades, multi-objective evolutionary algorithms (MOEAs) are developed as powerful tools to solve multi-objective optimization problems. While the diversity of Pareto front (PF) plays an important role in the performance evaluation of MOEAs, various diversity preservation strategies (DPS) have been developed. In this paper, a novel approach that inspired from the crowding distance technique is proposed to maintain the diversity of solutions in multi-objective problems (MOPs) with quite different spans of value range. In order to improve its performance, this approach is applied in a well-know MOEA NSGA II by replacing its original DPS. According to 3 test MOPs, the modified NSGA II shows a better diversity and distribution in the PF compared with the original version. Furthermore, the influence of the spans of value range on the performance of original DPS in NSGA II is discussed and the robustness of the new DPS is illustrated.

关键词： Multi-Objective Evolutionary Algorithm crowding distance diversity preservation NSGA II

作者: Yu Long Wang Pan Zhu Haoshen

作者单位: School of Automation Wuhan University of Technology Wuhan, P. R. China

会议类型: 国际会议

会议名称: 电子商务、工程及科学领域的分布计算和应用国际会议(DCABES 2010)

会议地点: 香港

会议语种:英文

页码: 98-102

在线出版日期: 2010-08-10（万方平台首次上网日期，不代表论文的发表时间）