An orthogonal-design hybrid particle swarm optimiser with application to capacitated facility location problem. (2016)

• Record Type: Journal Article
• Authors: Chu, Xianghua; Niu, Ben; Liang, J.J.; Lu, Qiang
• Is Part Of: International journal of bio-inspired computation. Volume 8:Number 5(2016)
• Page Start: 268
• Page End: 285
• Publication Date: 2016
• Subjects: particle swarm optimisation -- PSO -- global optimisation -- orthogonal arrays -- experimental design -- hybrid learning -- capacitated facility location -- population diversity -- bio-inspired computation; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

To improve the performance of particle swarm optimiser (PSO) for global optimisation, a variant called orthogonal-design hybrid particle swarm optimiser (OHPSO) is presented in this paper. A permutation strategy based on orthogonal experimental design is developed as a metabolic mechanism to enhance population diversity. In addition, a hybrid learning strategy is proposed to exploit the particles' best experiences and direct the individuals more efficiently. OHPSO is tested on a set of 18 benchmark functions with various properties, and nine state-of-the-art PSO variants are adopted for comparison. Experimental results and statistical analyses indicate a significant improvement of the proposed algorithm. Furthermore, OHPSO is applied to a practical engineering problem, the capacitated facility location problem, to justify its real-world performance and applicability. The experiment results are highly competitive with existing bio-inspired algorithms in the location optimisation.