Year: 2025
Type: article
Abstract: Recently, metaheuristic optimization algorithms have been studied and applied in many fields of engineering, especially in solving structural optimization problems. For structures with complex behaviors, the selection of a suitable optimization algorithm helps to significantly increase the computational efficiency and improve the accuracy of the solution. In this paper, we investigate the optimization of stiffened composite plates (SCP) using the Balancing Composite Motion Optimization (BCMO). The central concept of the BCMO algorithm is that its search process aims to find candidate solutions that maintain a good balance between global and local exploration within the solution space. The salient features of the algorithm are: (a) the candidate solutions can get closer to the best solutions and can explore the search space and (b) the method does not require algorithm parameters. The computational efficiency of the proposed framework is compared against other methods available in the literature, including GA, DE, and ReDE. Numerical results demonstrate that BCMO achieves optimal solutions comparable to or in close agreement with these established methods. Furthermore, the computational cost associated with BCMO, particularly the integer-variable version (Int_BCMO), proves significantly more cost-effective; for example, achieving CPU times less than 42% of GA in the square plate fiber angle optimization case and approximately three times faster than GA for thickness optimization problems. The numerical results from the proposed methodology are systematically validated and the robustness is demonstrated with a few examples. These findings underscore the efficiency and precision of the proposed approach in addressing optimization challenges related to stiffened composite plates.
DOI: 10.3934/jimo.2025119
Authors: Son Nguyen-Hoai, Trieu Vo L., Thuan Lam-Phat
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Trường Đại học Nguyễn Tất Thành
OpenAlex ID: W4412640744