Title: A modified particle swarm optimisation algorithm and its application in vehicle lightweight design

Authors: Zhao Liu; Ping Zhu; Chao Zhu; Wei Chen; Ren-Jye Yang

Addresses: The State Key Laboratory of Mechanical System and Vibration, Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ' The State Key Laboratory of Mechanical System and Vibration, Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ' The State Key Laboratory of Mechanical System and Vibration, Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ' Department of Mechanical Engineering, Northwestern University, 2145 Sheridan RD Tech B224, Evanston, IL 60201, USA ' Research and Advanced Engineering, Ford Motor Company, Dearborn, MI 48121, USA

Abstract: Particle swarm optimisation (PSO) is a global optimisation algorithm, which imitates the cooperation behaviour reflected in flocks of birds, fishes, etc. Because of its simple implementation and strong optimisation capacity, the PSO algorithm is becoming very popular in diverse engineering design applications. However, PSO is also seriously affected by the premature convergence problem similar to other global optimisation algorithms. It is generally known that diversity loss is one of the crucial impact factors. To improve the diversity of particles and enhance the algorithm's optimisation ability, the standard PSO algorithm is improved by a mutation operator, the optimal Latin hypercube design (OLHD) technique and boundary reflection method. Optimisation ability of the modified PSO is superior to the standard version through experimental comparison of eight benchmark functions. Combined with kriging surrogate model technique, the modified PSO algorithm is applied to a vehicle lightweight design problem. The frontal structure achieves 5.06 kg (13.95%) weight saving without performances loss after being optimised.

Keywords: PSO; particle swarm optimisation; OLHD; optimal Latin hypercube design; adaptive mutation operator; global optimisation; vehicle design; lightweight design; crashworthiness; swarm intelligence; metaheuristics; engineering design; boundary reflection method; kriging surrogate model; vehicle frontal structure; weight reduction.

DOI: 10.1504/IJVD.2017.082584

International Journal of Vehicle Design, 2017 Vol.73 No.1/2/3, pp.116 - 135

Received: 03 Jan 2015
Accepted: 16 Dec 2015
Published online: 02 Mar 2017 *

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