Structural Design Optimization Using Particle Swarm Optimization and Its Variants

Abstract

Structural design optimization has become an extremely challenging and more complex task for most real-world practical applications. A huge number of design variables and complex constraints have contributed to the complexity and nonlinearity of the problems. Mathematical programming and gradient-based search algorithms cannot be used to solve nonlinear problems. Thus, researchers have extensively conducted many experimental studies to address the growing complexity of these problems. Metaheuristic algorithms, which typically use nature as a source inspiration, have been developed over past decades. As one of the widely used algorithms, particle swarm optimization (PSO) has been studied and expanded to deal with many complex problems. Particle swarm optimization and its variants have great accuracy in finding the best solution while maintaining its fast convergence behavior. This study aims to investigate PSO and its variants to solve a set of complex structural optimization problems. Several complex benchmark studies of design problem were provided to study the performance of PSO, linearly decreasing inertia weight PSO and bare bones PSO. The results support the potential use of PSO and its variants as an alternative approach to solving structural design optimization problems.