Decomposition-based multi-objective comprehensive learning particle swarm optimisation Online publication date: Mon, 15-Apr-2019
by Xiang Yu; Hui Wang; Hui Sun
International Journal of Computational Science and Engineering (IJCSE), Vol. 18, No. 4, 2019
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.
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