Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) for Discrete and Continuous Size Optimization of 2D Truss Structures

Document Type : Regular Article

Authors

1 Assistant Professor, Civil Engineering Department, University of Kashan, Kashan, Iran

2 Ph.D. Candidate, Structure Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran

Abstract

Optimization of truss structures including topology, shape and size optimization were investigated by different researchers in the previous years. The aim of this study is discrete and continuous size optimization of two-dimensional truss structures with the fixed topology and the shape. For this purpose, the section area of the members are considered as the decision variables and the weight minimization as the objective function. The constraints are the member stresses and the node displacements which should be limited at the allowable ranges for each case. In this study, Genetic Algorithm and Particle Swarm Optimization algorithm are used for truss optimization. To analyse and determine the stresses and displacements, OpenSees software is used and linked with the codes of Genetic Algorithm and Particle Swarm Optimization algorithm provided in the MATLAB software environment. In this study, the optimization of four two-dimensional trusses including the Six-node, 10-member truss, the Eight-node, 15-member truss, the Nine-node, 17-member truss and the Twenty-node, 45-member truss under different loadings derived from the literature are done by the Genetic Algorithm and Particle Swarm Optimization algorithm and the results are compared with those of the other researchers. The comparisons show the outputs of the Genetic Algorithm are the most generally economical among the different studies for the discrete size cases while for the continuous size cases, the outputs of the Particle Swarm Optimization algorithm are the most economical.

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References

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  • Receive Date: 24 July 2019
  • Revise Date: 02 November 2019
  • Accept Date: 03 November 2019

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