An integrated particle swarm optimizer for optimization of truss structures with discrete variables

Mortazavi A., TOĞAN V., NUHOĞLU A.

STRUCTURAL ENGINEERING AND MECHANICS, vol.61, no.3, pp.359-370, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61 Issue: 3
  • Publication Date: 2017
  • Doi Number: 10.12989/sem.2017.61.3.359
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.359-370
  • Keywords: optimization, truss structures, particle swarm optimization, weighted particle, constraint handling, GENETIC ALGORITHM, TOPOLOGY OPTIMIZATION, DESIGN, STRATEGIES
  • Karadeniz Technical University Affiliated: Yes


This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.