A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
U-measure: a quality measure for multiobjective programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A numerical method for constructing the Pareto front of multi-objective optimization problems
Journal of Computational and Applied Mathematics
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In the case of multiobjective evolutionary algorithm, the outcome is usually an approximation of the true Pareto Optimal set and how to evaluate the quality of the approximation of the Pareto-optimal set is very important. In this paper, improved measures are carried out to the approximation, uniformity and well extended for the approximation of the Pareto optimal set with the advantage of easy to operate. Finally, we apply our measures to the four multiobjective evolutionary algorithms that are representative of the state-of-the-art on the standard functions. Results indicate that the measures are highly competitive and can be conducted to the comparisons of the approximation set.