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An efficient archive-based parameter-free multi-objective Rao-DE algorithm for bi-objective optimization of truss structures
Computers & Structures ( IF 4.4 ) Pub Date : 2025-01-08 , DOI: 10.1016/j.compstruc.2025.107647
Viet-Hung Truong, Sawekchai Tangaramvong, Hoang-Anh Pham, Manh-Cuong Nguyen, Rut Su

Metaheuristic algorithms have proven effective for complex optimization problems, including truss design, yet many require specific parameter settings, leading to increased complexity. This paper proposes an archive-based parameter-free multi-objective Rao-Differential Evolution (APMORD) algorithm for bi-objective optimization of truss design problems. APMORD simplifies the process by integrating the Rao-1 mutation technique with the differential evolution (DE) framework, eliminating the need for specific parameter setups. An external best archive (BA) enhances the diversity and distribution of the Pareto set, while the dynamic archive-based method (dynABM) adjusts the population size to improve optimization efficiency. The performance of APMORD is evaluated across eight classical truss structure problems using several indicators, showcasing its superior effectiveness compared to recent metaheuristic techniques, especially in achieving a broader spread of optimal solutions. Furthermore, sensitivity analysis indicates that decreasing the population size while increasing the archive size significantly enhances the algorithm’s performance and improves the quality of the optimal solution set. These findings highlight APMORD’s contribution to advancing optimization strategies for truss structures, emphasizing its efficiency and adaptability in various optimization scenarios.

中文翻译:


一种高效的基于存档的无参数多目标 Rao-DE 算法,用于桁架结构的双目标优化



事实证明,元启发式算法对于复杂的优化问题(包括桁架设计)有效,但许多算法需要特定的参数设置,从而导致复杂性增加。该文提出了一种基于档案的无参数多目标 Rao-Differential Evolution (APMORD) 算法,用于桁架设计问题的双目标优化。APMORD 通过将 Rao-1 突变技术与差分进化 (DE) 框架集成来简化流程,无需特定的参数设置。外部最佳存档 (BA) 增强了 Pareto 集的多样性和分布,而基于动态存档的方法 (dynABM) 调整了群体大小以提高优化效率。使用多个指标在八个经典桁架结构问题中评估 APMORD 的性能,展示了与最近的元启发式技术相比,其卓越的有效性,尤其是在实现更广泛的最优解决方案方面。此外,敏感性分析表明,在增加存档大小的同时减少群体大小会显著提高算法的性能并提高最佳解决方案集的质量。这些发现突出了 APMORD 对推进桁架结构优化策略的贡献,强调了其在各种优化场景中的效率和适应性。
更新日期:2025-01-08
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