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Title: Adaptive neighbourhood size adjustment in MOEA/D-DRA

Authors: Meng Xu; Maoqing Zhang; Xingjuan Cai; Guoyou Zhang

Addresses: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China; College of Computer Science and Technology, Beijing University of Technology, Beijing, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China

Abstract: Multi-objective optimisation algorithm based on decomposition (MOEA/D) is a well-known multi-objective optimisation algorithm, which was widely applied for solving multi-objective optimisation problems (MOPs). MOEA/D decomposes a multi-objective problem into a set of scalar single objective sub-problems using aggregation function and evolutionary operator. A further improved version of MOEA/D with dynamic resource allocation strategy (MOEA/D-DRA) has exhibited outstanding performance on CEC2009 in terms of the convergence. However, it is very sensitive to the neighbourhood size. In this paper, a new enchanted MOEA/D-ANA strategy based on the adaptive neighbourhood size adjustment (MOEA/D-ANA) was presented to increase the diversity, which mainly focuses on the solutions density around sub-problems. The experiment results demonstrate that MOEA/D-ANA performs the best compared with other five classical MOEAs on the CEC2009 test instances.

Keywords: MOEA/D; diversity; neighbourhood size; CEC2009 test instances.

DOI: 10.1504/IJBIC.2021.113336

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.1, pp.14 - 23

Accepted: 28 Jan 2019
Published online: 23 Feb 2021 *

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