Size and Topology Optimization of Trusses Using Hybrid Genetic-Particle Swarm Algorithms

Abstract

In this article, a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm (HGAPSO) are proposed to simultaneously optimize size and topology of trusses. The proposed hybrid algorithm simulates a mimetic-type behavior in which both genetic evolution and cultural information transfer are considered. GA performs genotypic inheritance, while PSO focuses on information transfer between population individuals. In the proposed method, the population members are divided into two equal numbered groups considering their fitness values. Then, the best half is sent to PSO for exploitation and the worst half is sent to GA to benefit from its exploration abilities. Several benchmark trusses are optimized using the proposed hybrid algorithm. The results are compared to those reported previously using other heuristic optimization methods. Comparisons demonstrate the efficiency, robustness and superior performance of the adopted HGAPSO. The proposed hybrid algorithm is also performed with a higher rate of convergence compared to other solutions.