Two-stage hybrid learning-based multi-objective evolutionary algorithm based on objective space decomposition

Abstract

Multi-objective evolutionary algorithm (MOEA) based on search space decomposition (MOEA/D-M2M) is a promising framework for solving multi-objective optimization problems (MOPs). It is crucial yet challenging for an MOEA to balance convergence and diversity. Few studies have been attempted to deal with the balancing problem under the MOEA/D-M2M framework. In this paper, a two-stage hybrid learning-based MOEA, dubbed HLMEA, is proposed to address this problem. In the first stage, we propose a genetic operator with an adaptive scaling parameter in order to accelerate the convergence of the search, while the environmental selection method maintains the diversity. In the second stage, the K-means clustering method is employed within each subpopulation to construct a mating pool for each individual. In each mating pool, an adaptive method based on hypervolume is proposed to choose a suitable differential evolution operator for offspring generation. Different from the first stage, the environmental selection method used in NSGA-II is applied to select a new population. Thereafter, subpopulations are obtained by dividing the newly created population. Extensive experimental results show that HLMEA has better performance than nine state-of-the-art MOEAs. The ablation study demonstrates the effectiveness of each component of HLMEA.