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

Zhao Liu, Ping Zhu*, Chao Zhu, Wei Chen, Ren Jye Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)116-135
Number of pages20
JournalInternational Journal of Vehicle Design
Issue number1
StatePublished - 2017


  • An adaptive mutation operator
  • Crashworthiness
  • Global optimisation
  • OLHD
  • Optimal Latin hypercube design
  • PSO
  • Particle swarm optimisation
  • Vehicle lightweight design

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering


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