The evolution of the Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm for optimizing truss structures.

Optimizing truss structures entails determining the most efficient arrangement and dimensions of members to fulfill specific goals, such as reducing weight and maximizing strength. Implementing the self-adaptive enhanced vibrating particle system (SA-EVPS) as a metaheuristic optimization technique for enhancing structural components in civil structures offers substantial potential for improving the efficiency and functionality of such components. This study presents a novel algorithm developed for optimizing the geometry and size of a 45-bar truss structure. Through extensive simulations and comparative analysis with seven recent metaheuristic algorithms, including the Whale Optimization Algorithm (WOA), the Marine Predators Algorithm (MPA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Moth-Flame Optimization (MFO), Grey Wolf Optimizer (GWO), and the Enhanced Vibrating Particle System (EVPS), the proposed algorithm demonstrates superior effectiveness in delivering enhanced structural performance by simultaneously optimizing member dimensions and structural geometry. The findings of this study indicate that the proposed SA-EVPS algorithm provides an effective and robust solution for improving the efficiency and reliability of structural optimization processes, with promising applicability to the optimization of a 45-bar truss structure. This advanced algorithm facilitates the identification of ideal geometries and member dimensions for structural components, considering factors such as load-bearing capacity and material optimization.