Treating Constraints as Objectives in Multiobjective Optimization Problems Using Niched Pareto Genetic Algorithm

Abstract

In this paper, the constraints, in multiobjective optimization problems, are treated as objectives. The constraints are transformed in two new objectives: one is based on a penalty function and the other is made equal to the number of violated constraints. To ensure the convergence to a feasible Pareto optimal front, the constrained individuals are eliminated during the elitist process. The treatment of infeasible individuals required some relevant modifications in the standard Parks and Miller elitist technique. Analytical and electromagnetic problems are presented and the results suggest the effectiveness of the proposed approach.