New Quality Measures for Multiobjective Programming


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.