Critical Evaluation of Metaheuristic Algorithms for Weight Minimization of Truss Structures

Abstract

This study critically compares variants of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Differential Evolution (DE), and Simulated Annealing (SA) used in truss sizing optimization problems including displacement and stress constraints. The comparison is based on several benchmark problems of varying complexity measured by the number of design variables and the degree of static indeterminacy. Most of these problems have been studied by numerous researchers using a large variety of methods; this allows for absolute rather than relative comparison. Rigorous statistical analysis based on large sample size, as well as monitoring of the success rate throughout the optimization process, reveal and explain the convergence behavior observed for each method. The results indicate that, for the problem at hand, Differential Evolution is the best algorithm in terms of robustness, performance, and scalability.