Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

Y. C. Lu, J. C. Jan, Shih-Lin Hung*, G. H. Hung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position- resetting. The purpose of the algorithm is to optimize the design of truss structures. Inspired by a heuristic, the boundary-shifting approach forces particles to move to the boundary between feasible and infeasible regions in order to increase the convergence rate in searching. The purpose of the particle-position-resetting approach, motivated by mutation scheme in genetic algorithms (GAs), is to increase the diversity of particles and to prevent the solution of particles from falling into local minima. The performance of the AugPSO algorithm was tested on four benchmark truss design problems involving 10, 25, 72 and 120 bars. The convergence rates and final solutions achieved were compared among the simple PSO, the PSO with passive congregation (PSOPC) and the AugPSO algorithms. The numerical results indicate that the new AugPSO algorithm outperforms the simple PSO and PSOPC algorithms. The AugPSO achieved a new and superior optimal solution to the 120-bar truss design problem. Numerical analyses showed that the AugPSO algorithm is more robust than the PSO and PSOPC algorithms.

Original languageEnglish
Pages (from-to)1251-1271
Number of pages21
JournalEngineering Optimization
Issue number10
StatePublished - 1 Oct 2013


  • optimization design
  • particle swarm optimization (PSO)
  • stochastic search method
  • truss structures


Dive into the research topics of 'Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures'. Together they form a unique fingerprint.

Cite this