S. Kukkonen and J. Lampinen (Finland)
multi-objective optimization, Pareto-optimization, con straints, evolution algorithms, differential evolution
In this paper an Evolutionary Algorithm, the Differen tial Evolution algorithm, and its extension for constrained multi-objective optimization are described. The described extension is tested with a set of five benchmark multi objective test problems and one constrained multi-objective test problem. Control parameter values for these test prob lems are surveyed and recommendations for initial control parameter values are concluded. The results are compared to known global Pareto-optimal fronts and to results ob tained with the Strength Pareto Evolutionary Algorithm in the case of benchmark problems. Results show that the extension is well comparable to the performance of the Strength Pareto Evolutionary Algorithm.
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