Improved Crowding Distance for NSGA-Ⅱ

摘要：

Non-dominated sorting genetic algorithm II (NSGA-Ⅱ) does well in dealing with multi-objective problems. When evaluating validity of an algorithm for multiobjective problems, two kinds of indices are often considered simultaneously, i.e. the convergence to Pareto Front and the distribution characteristic. The crowding distance in the standard NSGA-II has the property that solutions within a cubic have the same crowding distance, which has no contribution to the convergence of the algorithm. Actually the closer to the Pareto Front a solution is, the higher priority it should have. In the paper, the crowding distance is redefined while keeping almost all. the advantages of NSGA-II. Moreover, the speed of converging to the Pareto Front is faster. Finally, the improvement is proved to be effective by applying it to solve nine benchmark problems.

关键词： multi-objective NSGA-Ⅱ crowding distance

作者: Xiangxiang Chu Xinjie Yu

作者单位: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

会议类型: 国际会议

会议名称: The Second International Conference on Uncertainty Theory(ICUT)(第二届不确定理论国际会议)

会议地点: 拉萨

会议语种:英文

页码: 53-58

在线出版日期: 2011-08-06（万方平台首次上网日期，不代表论文的发表时间）