An Evolutionary Algorithm for Constrained Optimization


In this paper we present an evolutionary algorithm for constrained optimization. The algorithm is based on nondominance of solutions separately in the objective and constraint space and uses effective mating strategies to improve solutions that are weak in either. Since the methodology is based on nondominance, scaling and aggregation affecting conventional penalty function methods for constraint handling does not arise. The algorithm incorporates intelligent partner selection for cooperative mating. The diversification strategy is based on niching that result in a wide spread of solutions in the parametric space. Preliminary results of the algorithm for constrained single and multiobjective test problems are presented and compared to illustrate the efficiency of the algorithm in solving constrained optimization problems.