Differential evolution with the Adaptive Penalty Method for constrained multiobjective optimization

A differential evolution algorithm is proposed here to solve constrained multiobjective optimization problems (CMOPs). In this paper, an Adaptive Penalty Method (APM), which was successfully applied to solve single objective optimization problems, is used to handle the constraints. That constraint handling technique is incorporated to a multiobjective DE which combines the non-dominated ranking and crowding distance schemes when the candidate solutions are replaced. Previously, several variants of the APM were proposed and, here, those variants are tested in order to asses their performance when solving CMOPs. The results obtained in the computational experiments are used to compare the proposal with another well known constraint handling scheme in the literature.