A quality metric for multi-objective optimization based on hierarchical clustering techniques
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Defining and optimizing indicator-based diversity measures in multiobjective search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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In the research of MOEA (Multi-Objective Evolutionary Algorithm), many algorithms for multi-objective optimization have been proposed. Diversity of the solutions is an important measure, and it is also significant how to evaluate the diversity of an MOEA. In this paper, the clustering algorithm based on the distance between individuals is discussed, and a diversity metric based on clustering is also proposed. Applying this metric, we compare several popular multi-objective evolutionary algorithms. It is shown by experimental results that the method proposed in this paper performs well, especially helps to provide a comparative evaluation of two or more MOEAs.