Comparison of Multi-Objective Metaheuristics for Discrete Optimization of Steel Trusses Using Direct Analysis

This study enriches structural optimization research using direct analysis for steel truss structures, which is often hampered by high computational demands. The main objective of this work is to evaluate multi-objective optimization algorithms in truss sizing optimization with discrete variables, focusing on minimizing total mass and controlling inter-story drift under multiple load combinations. Five leading multi-objective metaheuristic algorithms were assessed: SPEA2, GDE3, NSGA2, MOEA/D, and the novel MOEA/D-EpDE, which uniquely combines MOEA/D with Dynamical Resource Allocation and pbest Differential Evolution. Four performance indicators, such as Generational Distance (GD), GD Plus (GD+), Inverted GD+ (IGD+), and Hypervolume (HV), were utilized. Findings from four truss optimization problems revealed that all considered algorithms located feasible optimal solutions, but MOEA/D-EpDE excelled, consistently securing the lowest GD, GD+, IGD+, and anchor point values, along with the highest HV values in most scenarios. This indicates its superior capability in addressing the problem efficiently. NSGA2 and MOEA/D also performed well, outperforming GDE3 and SPEA2. This study is pioneering in its application of these algorithms to steel truss optimization via direct analysis, highlighting the potential for advanced computational techniques in structural engineering.