Cooperative learning optimization algorithm for solving large-scale optimization problems

Fei Huang; Yifu Wang; Jiangang Tang

doi:10.1117/12.3035391

21 July 2024 Cooperative learning optimization algorithm for solving large-scale optimization problems

Fei Huang, Yifu Wang, Jiangang Tang

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132193T (2024) https://doi.org/10.1117/12.3035391
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

This paper proposes a metaheuristic optimization algorithm called Cooperative Learning Optimization algorithm (CLO). The algorithm simulates human cooperative behaviors, including imitative learning, creative learning, random learning, and selective learning, to establish a mathematical model for searching optimal solutions. The CLO algorithm is compared with other algorithms in 30-dimensional, and 500-dimensional optimization problems, demonstrating its robustness and ability to solve high-dimensional optimization problems. Finally, Friedman and Wilcoxon tests are conducted, showing significant differences between the CLO and the compared algorithms, highlighting the effectiveness of the CLO in optimization. The advantages of this algorithm lie in its simplicity, ease of operation, and good convergence performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fei Huang, Yifu Wang, and Jiangang Tang "Cooperative learning optimization algorithm for solving large-scale optimization problems", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132193T (21 July 2024); https://doi.org/10.1117/12.3035391

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