A Novel Multi-Objective Evolutionary Algorithm Based on External Dominated Clustering
Lei Fan, Yuping Wang · 2008
Evolutionary algorithms (EAs) have wide applications in practice and many advantages over traditional methods in solving nonlinear and complex optimal problems. In this paper, we propose a novel clustering technique, in which the infeasible solutions are employed to divide the feasible solutions into several clusters. There is no more one infeasible individual in each cluster. A novel evolutionary algorithm based on this technique called ED-MOEA is proposed for dealing with constrained multi-objective problems. Simulation results on five test problems indicate the proposed algorithm is effective.