Indicator-based weight adaptation for solving many-objective optimization problems

Abstract

Weight adaptation methods can enhance the diversity of solutions obtained by decomposition-based approaches when addressing irregular Pareto front shapes. Generally, these methods adapt the location of each weight vector during the search process. However, early adaptation could be unnecessary and ineffective because the population does not provide a good Pareto front approximation at early generations. In order to improve its performance, a better approach would be to trigger such adaptation only when the population has reached the Pareto front. In this paper, we introduce a performance indicator to assist weight adaptation methods, called the median of dispersion of the population (MDP). The proposed indicator provides a general snapshot of the progress of the population toward the Pareto front by analyzing the local progress of each subproblem. When the population becomes steady according to the proposed indicator, the adaptation of weight vectors starts. We evaluate the performance of the proposed approach in both regular and irregular test problems. Our experimental results show that the proposed approach triggers the weight adaptation when it is needed.