Competition of tribes and cooperation of members algorithm: An evolutionary computation approach for model free optimization

Metaheuristic algorithms solve optimization problems mostly by imitating behaviors observed in nature. Over time, these algorithms have proven to be very effective in solving complex optimization problems. Due to the rising complexity and scale of practical engineering problems, numerous metaheuristic algorithms have been developed recently and applied in various fields. In response to this need, researchers continue to explore novel approaches inspired by natural and social phenomena. Inspired by the competition among ancient tribes and their cooperative behavior, this paper proposes a meta-heuristic called the Competition of Tribes and Cooperation of Members Algorithm (CTCM). Experiments are conducted on 23 benchmark test functions and comprehensively compared with other state-of-the-art algorithms, including particle swarm optimization (PSO), grey wolf optimizer (GWO), sparrow search algorithm (SSA), egret swarm optimization (ESOA), beetle antennae search (BAS) and whale optimization (WOA). The standard deviation and average, as well as statistical tests are utilized to compare the performance of each algorithm, which demonstrates that CTCM is superior in the majority of problems. In addition, the results of Wilcoxon and Friedman rank tests show that the CTCM achieves the first place in all categories of problems. The results indicate that CTCM possesses strong global optimization search capability and stability, and has faster convergence speed. The paper also considers solving practical engineering optimization problems as proof-of-concept case studies, in which CTCM achieves all the optimal solutions for each engineering problem.