A modified particle swarm optimizer and its application to spatial truss topological optimization

Abstract

Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which is inspired by concepts from 'Social Psychology' and 'Artificial Life'. Essentially, PSO proposes that the co-operation of individuals promotes the evolution of the swarm. In terms of optimization, the hope would be to enhance the swarm's ability to search on a global scale so as to determine the global optimum in a fitness landscape. It has been empirically shown to perform well with regard to many different kinds of optimization problems. PSO is particularly a preferable candidate to solve highly nonlinear, non-convex and even discontinuous problems. In this paper, one enhanced version of PSO: Modified Lbest based PSO (LPSO) is proposed and applied to one of the most challenging fields of optimization -- truss topological optimization. Through a benchmark test and a spatial structural example, LPSO exhibited competitive performance due to improved global searching ability.