A quantum-inspired particle swarm optimization for sizing optimization of truss structures

Abstract

Particle swarm optimization (PSO) algorithm has some advantages such as simple principle, less model parameters. However, it has some flaws for dealing with sizing optimization of truss structures, such as low accuracy, slow astringency and poor population diversity in the later evolution stage. In order to overcome these shortcomings and deal with optimization problem of truss structures effectively, a new quantum-inspired particle swarm optimization (QPSO) algorithm with quantum angle encoding and a perturbation operator of the population is proposed in this paper. Then, QPSO is used for optimization of truss structures by means of penalty function method. In the end, two typical numerical tests for truss design are performed to calibrate and verified the proposed algorithm. The results of the QPSO compared with PSO and other optimization algorithms show that the proposed QPSO has a better performance in terms of both the optimum solutions and the convergence capability than PSO and is a powerful optimization technique for solving optimization problem of truss structures.