Decomposition-Based Multiobjective Evolutionary Algorithm With Genetically Hybrid Differential Evolution Strategy

Abstract

In the decomposition-based multiobjective evolutionary algorithms (MOEA/Ds), a set of subproblems are optimized by using the evolutionary search to exploit the feasible regions. In recent studies of MOEA/Ds, it was found that the design of recombination operators would significantly affect their performances. Therefore, this paper proposes a novel genetically hybrid differential evolution strategy (GHDE) for recombination in MOEA/Ds, which works effectively to strengthen the search capability. Inspired by the existing studies of recombination operators in MOEA/Ds, two composite operator pools are introduced, each of which includes two distinct differential evolution (DE) mutation strategies, one emphasizing convergence and the other focusing on diversity. Regarding each selected operator pool, two DEs are applied on parents' genes to hybridize offspring by adaptive parameters tuning. Moreover, a fitness-rate-rank-based multiarmed bandit (FRRMAB) is embedded into our algorithm to select the best operator pool by collecting their recently achieved fitness improvement rates. After embedding GHDE into an MOEA/D variant with dynamical resource allocation, a variant named MOEA/D-GHDE is presented. Various test multiobjective optimization problems (MOPs), i.e., UF, F test suites, and MOPs with difficult-to-approximate (DtA) PF boundaries, are used to assess performances. Compared to several competitive MOEA/D variants, the comprehensive experiments validate the superiority of our algorithm.

Keywords

• Multiobjective optimization
• decomposition
• recombination operator
• differential evolution