Sizing Optimization of Trusses Using Elitist Stepped Distribution Algorithm
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This study investigates the efficiency of the recently developed Elitist Stepped Distribution Algorithm (ESDA) as a metaheuristic framework for truss sizing optimization. ESDA builds upon the Cross-Entropy Method by introducing an elitist stepped sampling strategy that improves the balance between exploration and exploitation during the search process. To evaluate its effectiveness, ESDA is applied to a comprehensive test suite comprising seven benchmark truss optimization problems that cover a wide range of sizes, design variables, loading conditions, and constraint types. In all cases, the objective is to minimize structural weight while satisfying stress, displacement, and stability requirements. Numerical experiments are conducted with the proposed method, and the results are compared with those algorithms reported in the literature. The findings show that ESDA attains new best or near-best solutions for large-scale problems such as the 117-bar cantilever, 130-bar transmission tower, 354-bar dome, and 942-bar tower trusses, while also producing competitive results for the 25-bar, 72-bar, and 200-bar structures with relatively modest computational effort. The novelty of this work lies in demonstrating the robustness, efficiency, and scalability of ESDA across diverse benchmarks, highlighting its potential for future structural optimization applications.
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