一种多目标进化算法的分布度评价方法

A Diversity Metric for Multi-Objective Evolutionary Algorithm

系统分析现存多目标进化算法中分布度评价方法的特点和不足,提出一种基于最小生成树的可变邻域分布度评价方法,通过评价解集在"邻域"内的相对均匀程度,准确给出解集的分布结果,并部分解决现有方法不能对Pareto 最优面为非均匀分布的测试函数评价的问题.另外,给出一种解集映射方法,使其在少考虑一维信息同时,保持分布情况不变.实验结果证明该方法的可行性和有效性.

A measurement of evaluating the diversity of non-dominated solutions in the objective space is introduced. It constructs alterable neighborhoods of solutions and the sizes of these neighborhoods change with the density of solution sets. The diversity relations among these neighborhoods are computed, and a metric is build. The metric can be used to compare the performance of different multi-objective optimization techniques. In particular, it can adapt to uniform test problems and non-uniform test problems. Experimental results show the proposed measurement is effective.