Comparison of meta-heuristic algorithms on the size and layout optimization of truss structures
Abstract
Truss structures constitute integral components of civil engineering projects, necessitating engineers to achieve optimal designs balancing material cost and structural capacity. Traditional gradient-based optimization methods often face challenges in nonlinear and non-convex optimization scenarios, leading to prolonged convergence times. Meta-heuristic algorithms present viable alternatives for optimizing the layout and dimensions of truss structures under such conditions. This study focuses on optimizing the sizes and configurations of three distinct planar benchmark truss structures using three different meta-heuristic optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Adaptive Geometry Estimation based MOEA (AGE-MOEA). The optimization results for the Planar 10-bar truss structure indicated that PSO slightly outperformed GA and AGE-MOEA by achieving the lowest weight of 5065.33 lb. For the 15-bar truss structure, GA achieved the lowest weight of 79.74 lb, demonstrating its effectiveness. In the case of the 18-bar truss structure, PSO again showed superior performance with the lowest weight of 4523.57 lb. Through comparative analysis of convergence rates and optimal solutions derived from these algorithms, this research evaluates their effectiveness in addressing the complexities of truss structural optimization. The findings suggest that while all three algorithms are effective, PSO often provides the most efficient solutions in terms of weight minimization for complex truss structures.
Keywords
Structural design , Truss optimization , Meta-heuristic algorithms
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