A novel enhanced competition of tribes and cooperation of members algorithm for global optimization

Abstract

The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. However, when dealing with certain complex optimization problems, the algorithm may converge to a local optimal solution prematurely, thereby failing to reach the global optimal solution. To enhance the algorithm’s global optimization capabilities and stability, an enhanced CTCM (CTCMKT) is proposed, which integrates a joint strategy of Kent chaotic mapping and t- distribution mutation. This integration effectively prevents premature convergence to local optimal solutions, ensuring that the algorithm does not miss the global optimal solution during the optimization process and the algorithm’s stability is significantly enhanced. CEC2021 and 23 benchmark functions are used to test the effectiveness and feasibility of the CTCMKT. By minimizing the fitness value, the CTCMKT is contrasted with other algorithms. Experimental results reveal that the CTCMKT has a superior global optimization ability compared to these algorithms. It can efficiently balance exploration and exploitation to reach the optimal solution. Additionally, the CTCMKT can effectively boost the convergence speed, calculation accuracy, and stability. Engineering application results show that the improved CTCMKT algorithm can solve practical application problems.