Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Minimal sets of quality metrics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
U-measure: a quality measure for multiobjective programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computers and Operations Research
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Due to the big success of the Pareto's Optimality Criteria for multi-objective problems, an increasing number of algorithms that use it have been proposed. The goal of these algorithms is to find a set of non-dominated solutions that are close to the True Pareto front. As a consequence, a new problem has arisen, how can the performance of different algorithms be evaluated? In this paper, we present a novel system to evaluate m non-dominated sets, based on a few assumptions about the preferences of the decision maker. In order to evaluate the performance of our approach, we build several test cases considering different topologies of the Pareto front. The results are compared with those of another popular metric, the S-metric, showing equal or better performance.