A Cost Value Based Evolutionary Many-Objective Optimization Algorithm with Neighbor Selection Strategy

Abstract

Based on the ideas of minimizing the loss of convergence and diversity of the candidate solution set, this paper proposes a cost value based evolutionary many-objective algorithm with neighbor selection strategy. In this work, the cost value of each solution is the mutual evaluation from other ones in current population. By this way, the proposed algorithm, named MEMO, can easily recognize the dominated and the nondominated solutions and assess the contribution of convergence and diversity of each solution among the candidate solution set. To further enhance the performance of proposed algorithm, a neighbor selection strategy is also suggested in this paper. Simulation experiments on MaF series indicate that the proposed MEMO is superior to IBEA, MOEA/D, NSGA-III and RVEA in terms of effectiveness and robustness.