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In this study, the cost optimization of triangular section reinforced concrete (RC) beams, which are used in buildings, is performed using metaheuristic algorithms that are Differential Evolution (DE), Jaya Algorithm (JA), Teaching–Learning Based Optimization (TLBO), Flower Pollination Algorithm (FPA), Harmony Search (HS), Grey Wolf Optimization (GWO) and Bat Algorithm (BA). This process, carried out by comparing the optimal design according to the boundary conditions considering the regulations, has an important place in comparing different algorithms. For some complex problems, the superiority of some algorithms is partially demonstrated by numerical examples and by running the algorithm for different numbers of iterations. Although there are algorithms that attain different objective functions at different iterations, TLBO, GWO and FPA are calculated to be close to the objective function at low iteration numbers. When other iteration numbers such as 5, 7, 10, 15, 20, 30, 50, 70 as well as 100 are considered, it is observed that TLBO, JA and GWO are closer to the objective function than the other algorithms. This is indicated by the mean and standard deviation of these iteration numbers.

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Optimization of Triangular Reinforced Concrete Beam with Various Metaheuristic Algorithms

  • 章节

  • 发表日期：

    2026年

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  • Muhammed Çoşut,
  • Sinan Melih Nigdeli,
  • Gebrail Bekdaş

摘要

In this study, the cost optimization of triangular section reinforced concrete (RC) beams, which are used in buildings, is performed using metaheuristic algorithms that are Differential Evolution (DE), Jaya Algorithm (JA), Teaching–Learning Based Optimization (TLBO), Flower Pollination Algorithm (FPA), Harmony Search (HS), Grey Wolf Optimization (GWO) and Bat Algorithm (BA). This process, carried out by comparing the optimal design according to the boundary conditions considering the regulations, has an important place in comparing different algorithms. For some complex problems, the superiority of some algorithms is partially demonstrated by numerical examples and by running the algorithm for different numbers of iterations. Although there are algorithms that attain different objective functions at different iterations, TLBO, GWO and FPA are calculated to be close to the objective function at low iteration numbers. When other iteration numbers such as 5, 7, 10, 15, 20, 30, 50, 70 as well as 100 are considered, it is observed that TLBO, JA and GWO are closer to the objective function than the other algorithms. This is indicated by the mean and standard deviation of these iteration numbers.