Title: Decomposition-based multi-objective comprehensive learning particle swarm optimisation

Authors: Xiang Yu; Hui Wang; Hui Sun

Addresses: Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China ' Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China ' Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China

Abstract: This paper proposes decomposition-based comprehensive learning particle swarm optimisation (DCLPSO) for multi-objective optimisation. DCLPSO uses multiple swarms, with each swarm optimising a separate objective. Two sequential phases are conducted: independent search and then cooperative search. Important information related to extreme points of the Pareto front often can be found in the independent search phase. In the cooperative search phase, a particle randomly learns from its personal best position or an elitist on each dimension. Elitists are non-dominated solutions and are stored in an external repository shared by all the swarms. Mutation is applied to each elitist in this phase to help escaping from local Pareto fronts. Experiments conducted on various benchmark problems demonstrate that DCLPSO is competitive in terms of convergence and diversity of the resulting non-dominated solutions.

Keywords: particle swarm optimisation; PSO; comprehensive learning; multi-objective optimisation; decomposition.

DOI: 10.1504/IJCSE.2019.099073

International Journal of Computational Science and Engineering, 2019 Vol.18 No.4, pp.349 - 360

Received: 24 May 2016
Accepted: 22 Sep 2016
Published online: 15 Apr 2019 *

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