Cooperative learning optimization algorithm for solving large-scale optimization problems

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.