Title: A coevolutionary algorithm using multi-operator ensemble for many-objective optimisation problems

Authors: Di Zhu; Renbin Xiao; Gui Li; Yingnan Ma; Mengting Yi

Addresses: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei – 430074, China ' School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei – 430074, China ' School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei – 430074, China ' School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei – 430074, China ' School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei – 430074, China

Abstract: MaOPs are typically solved by using evolutionary algorithms (EAs) to search solutions with the help of operators. The strategy of multiple-operator ensemble (MOE) can combine the search capabilities of different operators to ensure better adaptability in different fitness landscapes. This paper proposes a MaOEA/D algorithm based on coevolutionary multi-operator ensemble (MaOEA/D-CME) for solving MaOPs. The algorithm utilises coevolution technique to balance the capabilities of the simulated binary crossover operator (SBX) and the differential evolution operator (DE) in MOEA/D for different types of problems. To reduce computational costs and avoid premature convergence or slow convergence, we propose a 'multi-stage environmental selection' strategy. Tested on benchmark problems of 13 challenging high-dimensional MaOPs, the numerical results in terms of HV and IGD indicators demonstrate that MaOEA/D-CME achieves competitive advantages compared to some state-of-the-art MOEAs.

Keywords: many-objective optimisation; shift-based density estimation; multiple-operator ensemble; MOE; decomposition; co-evolution.

DOI: 10.1504/IJBIC.2024.141689

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.3, pp.191 - 200

Received: 07 May 2024
Accepted: 30 Jun 2024
Published online: 30 Sep 2024 *

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