Recent metaheuristic algorithms for solving some civil engineering optimization problems.

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.