Performance assessment of multiobjective optimizers: an analysis and review

作者：Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M. Fonseca, Viviane Grunert da Fonseca · 发表于：IEEE Transactions on Evolutionary Computation · 年份：2003 · DOI：10.1109/tevc.2003.810758 · 被引用次数：4350 · 研究领域：Advanced Multi-Objective Optimization Algorithms、Evolutionary Algorithms and Applications、Metaheuristic Optimization Algorithms Research

An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.