基于Delaunay三角剖分的多目标进化算法解集分布度评价指标

A Delaunay Triangulation Based Diversity Metric for Solution Set of Multi-Objective Evolutionary Algorithms

系统分析目前多目标进化算法(MOEAs)分布度评价指标的特点和不足,提出一种基于Delaunay三角剖分的分布度评价指标.该指标将基于邻域和基于距离的评价思想相结合,利用Delaunay三角网最近邻与邻接性的特点实现自主邻域划分.采用空间映射的方法,有效减少MOEAs解集非支配关系对种群分布度评价的影响.测试结果表明该指标能准确反映MOEAs解集的分布性.

A Delaunay triangulation based metric （DTDM） is proposed for assessing the diversity metric in multi-objective evolutionary algorithms （MOEAs） by analyzing the characteristics and shortcomings of the current diversity metrics. The proposed metric is introduced by combining the neighborhood-based ideology and distance-based ideology. The metric independently searches the neighborhood by using the properties of the nearest and adjacent neighborhood of Delaunay triangulation net. The non-dominated relationship is eliminated according to a space mapping technique. The experimental results show that the proposed metric accurately evaluates the diversity of the solution set obtained by MOEAs.