Constrained optimization based on a multiobjective evolutionary algorithm

A criticism of evolutionary algorithms (EAs) might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods. EAs have received increased interest during the last decade due to the ease of handling multiple objectives. A constrained optimization problem or an unconstrained multiobjective problem may in principle be two different ways to pose the same underlying problem. In this paper, an alternative approach for the constrained optimization problem is presented. The method is a variant of a multiobjective real coded genetic algorithm (GA) inspired by the penalty approach. It is evaluated on six different constrained single objective problems found in the literature. The results show that the proposed method performs well in terms of efficiency, and that it is robust for a majority of the test problems.