The entropy metric in diversity of Multiobjective Evolutionary Algorithms

Abstract

Quality assessment of Multiobjective Optimization Evolutionary Algorithms (MOEA) has been a major concern in scientific field during the last few years. The entropy metric is introduced and highlighted in computing the diversity of MOEA. Existing performance metrics are briefly reviewed. The advantages of entropy approach over other diversity metrics are classified here. Besides, the definition of the entropy metric and the approach of measuring diversity based on entropy are presented, in which section a detailed explanation about how to choose appropriate parameters in entropy measurement and programming techniques are given. Above all, some significant conclusions arised from the further study of this entropy metric are summarized, which may have a meaningful impact on the high-efficient comparisons of MOEA performance on a quantitative basis. Last but not the least, it will help researchers to determine whether or not a population of solutions has reached maturity, in order to end the optimization process evaluation process.