Abstract
In order to overcome the premature convergence defect of the basic particle swarm optimization (PSO) algorithm and provide an effective method for shape and sizing optimization of truss structure, an improved PSO was proposed. The random direction method was employed to produce high-quality initial population, the fuzzy system was applied in the dynamic adaptive adjustment of parameters of the PSO, and the Metropolis criteria were used to improve the performance of PSO. Then, the improved PSO was introduced to the truss structure shape and sizing optimization design. Engineering practice and comparison with the other optimization algorithms show that the algorithm has good convergence and global searching capability. The study provides a promising algorithm for the structural optimization.
Highlights
The engineering structure optimization can improve the design quality, shorten the design cycle, and cut down the engineering cast (Cai et al 2011; Gandomi, Yang 2011)
In order to validate the efficiency of the improved particle swarm optimization (PSO) we proposed, we employed it to optimize two typical functions with multiple extreme values and compared the results with the original PSO algorithm
In order to verify the efficiency of the improved PSO in truss structure optimization design, we employed it to solve the problem of truss structure shape optimization shown in Wang et al (2002a)
Summary
The engineering structure optimization can improve the design quality, shorten the design cycle, and cut down the engineering cast (Cai et al 2011; Gandomi, Yang 2011). PSO was introduced by Kennedy and Eberhart (1995) They pointed out that the rules which govern the movement of the particles in a problem’s search space can be seen as a model of human social behavior in which individuals adjust their beliefs and attitudes to conform with those of their peers. It has roots in the simulation of social behaviors, in particular the dynamic theory of social impact (Kennedy 2006; Nowak et al 1990), using tools and ideas taken from computer graphics and social psychology research. Where i is the number of a particle, n is the dimension of the problem solved, j is the number of routine generation, w is the inertia weight, c1 and c2 are acceleration coefficients, rand1j and rand2j are the random numbers in the range of [0, 1]
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