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  3. Recent Metaheuristic Algorithms For Solving Some Civil Engineering Optimization Problems.
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  3. Recent Metaheuristic Algorithms For Solving Some Civil Engineering Optimization Problems.

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Recent metaheuristic algorithms for solving some civil engineering optimization problems.

Essam H Houssein1,2, Mohamed Hossam Abdel Gafar3, Naglaa Fawzy3

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt. essam.halim@mu.edu.eg.

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|March 6, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new hybrid metaheuristic algorithm, Bald Eagle Search-Growth Optimizer (BES-GO), excels at structural design optimization. It demonstrates superior performance in solving complex engineering problems compared to existing methods.

Keywords:
Blade eagle searchCivil engineeringMetaheuristic optimization algorithmsWelded beam design

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Area of Science:

  • Engineering Optimization
  • Computational Intelligence
  • Structural Engineering

Background:

  • Structural design optimization problems present significant computational challenges.
  • Existing metaheuristic algorithms often face limitations in convergence speed and solution accuracy.
  • There is a continuous need for advanced algorithms to address complex engineering design tasks.

Purpose of the Study:

  • To propose a novel hybrid metaheuristic algorithm, Bald Eagle Search-Growth Optimizer (BES-GO).
  • To evaluate the effectiveness of BES-GO on benchmark structural design optimization problems.
  • To compare the performance of BES-GO against ten state-of-the-art metaheuristic algorithms.

Main Methods:

  • Development of the hybrid BES-GO algorithm by integrating Bald Eagle Search (BES) and Growth Optimizer (GO).
  • Application of BES-GO to diverse structural design problems: welded beam, three-bar truss, I-beam deflection, tubular columns, and cantilever beams.
  • Performance evaluation using the CEC'20 test suite and comparison with ten other metaheuristic algorithms.

    • Main Results:

    • The BES-GO algorithm consistently achieved superior results compared to the ten benchmarked algorithms.
    • BES-GO demonstrated enhanced search capabilities and faster convergence rates.
    • The algorithm proved effective in finding optimal or near-optimal solutions for all tested structural design problems.

    Conclusions:

    • The proposed BES-GO hybrid algorithm is a robust and effective tool for structural design optimization.
    • BES-GO offers significant advantages in terms of convergence speed and solution quality.
    • This novel approach provides a promising direction for future research in computational intelligence for engineering.