ArticleScientific reports2025
Recent metaheuristic algorithms for solving some civil engineering optimization problems.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Enhanced Osprey Optimization Algorithm for Global Optimization with Application to PEM Fuel Cell Parameter Identification.Biomimetics (Basel, Switzerland) · 2026Article
- Constrained speed reducer design optimization using a novel Scuba Diver Optimization Algorithm.Scientific reports · 2026Article
- Fitness Distance Balanced Starfish Optimization for Benchmark and Engineering Design Problems.Biomimetics (Basel, Switzerland) · 2026Article
- Leveraging hybrid deep learning with starfish optimization algorithm based secure mechanism for intelligent edge computing in smart cities environment.Scientific reports · 2025Article
- Deep fusion based transfer learning with bald eagle search algorithm for sign language recognition to assist individuals with hearing and speech impairments.Scientific reports · 2025Article
- Quadrilateral mesh optimisation method based on swarm intelligence optimisation.Scientific reports · 2025Article
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4 authors.
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Abstract
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.
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