Volume 15, Issue 2 (4-2025)
IJOCE 2025, 15(2): 259-278 |
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1- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Abstract: (12397 Views)
Metaheuristic algorithms mostly consist of some parameters influencing their performance when faced with various optimization problems. Therefore, this paper applies Multi-Stage Parameter Adjustment (MSPA), which employs Extreme Latin Hypercube Sampling (XLHS), Primary Optimizer, and Artificial Neural Networks (ANNs) to a recently developed algorithm called the African Vulture Optimization Algorithm (AVOA) and a well-known one named Particle Swarm Optimization (PSO) for tuning their parameters. The performance of PSO is tested against two engineering and AVOA for two structural optimization problems, and their corresponding results are compared to those of their default versions. The results showed that the employment of MSPA improved the performance of both metaheuristic algorithms in all the considered optimization problems.
Keywords: Metaheuristics, Optimization, Multi-Stage Parameter Adjustment, Artificial Neural Networks, Extreme Latin Hypercube Sampling, AVOA, PSO
Type of Study: Research |
Subject:
Optimal design
Received: 2025/04/24 | Accepted: 2025/06/28 | Published: 2025/06/30
Received: 2025/04/24 | Accepted: 2025/06/28 | Published: 2025/06/30
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