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

Multi-objective evolutionary algorithm (MOEA) based on search space decomposition (MOEA/D-M2M) is a promising framework for solving multi-objective optimization prob-lems (MOPs). It is crucial yet challenging for an MOEA to balance convergence and diver-sity. 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 oper-ator 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.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

In this paper, a two-stage hybrid learning-based multi-objective evolutionary algorithm is proposed to address the balancing problem between convergence and diversity in multi-objective optimization problems. Experimental results show that the proposed algorithm outperforms nine state-of-the-art algorithms.