Houssein Essam H, Hossam Abdel Gafar Mohamed, Fawzy Naglaa, Sayed Ahmed Y
Faculty of Computers and Information, Minia University, Minia, Egypt.
Minia National University, Minia, Egypt.
Sci Rep. 2025 Mar 7;15(1):7929. doi: 10.1038/s41598-025-90000-8.
In this study, a novel hybrid metaheuristic algorithm, termed (BES-GO), is proposed for solving benchmark structural design optimization problems, including welded beam design, three-bar truss system optimization, minimizing vertical deflection in an I-beam, optimizing the cost of tubular columns, and minimizing the weight of cantilever beams. The performance of the proposed BES-GO algorithm was compared with ten state-of-the-art metaheuristic algorithms: Bald Eagle Search (BES), Growth Optimizer (GO), Ant Lion Optimizer, Tuna Swarm Optimization, Tunicate Swarm Algorithm, Harris Hawk Optimization, Artificial Gorilla Troops Optimizer, Dingo Optimizer, Particle Swarm Optimization, and Grey Wolf Optimizer. The hybrid algorithm leverages the strengths of both BES and GO techniques to enhance search capabilities and convergence rates. The evaluation, based on the CEC'20 test suite and the selected structural design problems, shows that BES-GO consistently outperformed the other algorithms in terms of convergence speed and achieving optimal solutions, making it a robust and effective tool for structural Optimization.
在本研究中，提出了一种名为（BES-GO）的新型混合元启发式算法，用于解决基准结构设计优化问题，包括焊接梁设计、三杆桁架系统优化、最小化工字梁的垂直挠度、优化管柱成本以及最小化悬臂梁的重量。将所提出的BES-GO算法的性能与十种先进的元启发式算法进行了比较：秃鹰搜索（BES）、生长优化器（GO）、蚁狮优化器、金枪鱼群优化、被囊动物群算法、哈里斯鹰优化、人工大猩猩部队优化器、澳洲野犬优化器、粒子群优化和灰狼优化器。该混合算法利用了BES和GO技术的优势，以提高搜索能力和收敛速度。基于CEC'20测试套件和所选结构设计问题的评估表明，BES-GO在收敛速度和获得最优解方面始终优于其他算法，使其成为结构优化的强大而有效的工具。
