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Advancing hierarchical optimization: A-cubed algorithm for adaptive agent collaboration
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AI
DOI：10.1016/j.matcom.2025.07.024.png)
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Abstract
En 中文
• This study introduces the Agent Assembly Algorithm (A-Cubed), a novel metaheuristic inspired by elite adaptive societies that enhances hierarchical optimization strategies. • It employs a distinctive hierarchical framework to dynamically manage the search behaviors of agents dynamically, thereby improving the balance between exploration and exploitation in optimization tasks. • The performance of A-Cubed is shown to be superior across three rigorous benchmarks, which include 50 mathematical benchmarks, 12 IEEE CEC 2022 functions, and practical topology optimization scenarios. • A-Cubed achieves optimal solutions with fewer objective function evaluations and higher convergence rates in complex engineering problems compared to traditional methods. • The effectiveness of A-Cubed is validated in various numerical optimization challenges, suggesting potential applications in diverse engineering and computational fields.
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