Title: An orthogonal-design hybrid particle swarm optimiser with application to capacitated facility location problem

Authors: Xianghua Chu; Ben Niu; J.J. Liang; Qiang Lu

Addresses: Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China ' College of Management, Shenzhen University, Shenzhen 518060, Guangdong, China ' School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China ' Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China

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.

Keywords: particle swarm optimisation; PSO; global optimisation; orthogonal arrays; experimental design; hybrid learning; capacitated facility location; population diversity; bio-inspired computation.

DOI: 10.1504/IJBIC.2016.079568

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.5, pp.268 - 285

Received: 08 Jul 2013
Accepted: 06 Oct 2013
Published online: 04 Oct 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article