Discrete Optimization of Steel Trusses using Chaotic Center of Mass Optimization Algorithm

This study introduces a novel metaheuristic optimization algorithm called Chaotic Center of Mass Optimization (CCMO) for the discrete optimization of steel truss structures. CCMO enhances the traditional Center of Mass Optimization (CMO) by incorporating chaotic maps, improving the balance between exploration and exploitation. This helps the algorithm avoid local optima and accelerate convergence, making it effective for complex structural problems. The algorithm is tested on four benchmark truss optimization problems (۱۰-bar, ۲۵-bar, ۵۲-bar, and ۷۲-bar trusses) under various loading conditions and design constraints, with the goal of minimizing structural weight while adhering to stress, displacement, and stability requirements. CCMO is compared to several established algorithms, including Harmony Search (HS), Particle Swarm Optimization (PSO), Enhanced Harmony Search (EHS), Teaching-Learning-Based Optimization (TLBO), and standard CMO. The results show that CCMO consistently provides optimal or near-optimal solutions with lower computational costs and higher stability. It requires fewer structural analyses, indicating superior computational efficiency, and demonstrates robustness by achieving low standard deviations across multiple runs. Unlike other algorithms, CCMO consistently produces fully feasible solutions without constraint violations. Overall, the study concludes that CCMO is an effective and efficient method for steel truss design, offering high-quality solutions with minimal computational effort. Future research could extend its application to larger-scale and multi-objective optimization problems.

کلیدواژه ها:

Discrete Optimization ، Steel Trusses ، Chaotic Center of Mass Optimization Algorithm ، Metaheuristics