An Improved Differential Evolution Algorithm for Multi-objective Optimization Problems

Abstract

In this paper, an improved multi-objective differential evolution algorithm(IDEA) is proposed for multi-objective optimization problems. In IDEA, the select operator combines the advantages of DE with the mechanisms of Pareto-based ranking and distance density, besides, a randomly migration strategy is proposed. IDEA is implemented on four classical multi-objective problems, the simulation results indicate that the proposed IDEA efficiently achieves two goals of multi-objective optimization problems: find the solutions converse to the true Pareto-front and uniform spread along the front.