Research on Chaotic Chimp Optimization Algorithm Based on Adaptive Tuning and Its Optimization for Engineering Application

Abstract

The original Chimp Optimization Algorithm has disadvantages such as slow convergence, the tendency to fall into local optima, and low accuracy in finding the best. To alleviate the existing problems, a chaotic chimp optimization algorithm based on adaptive tuning is proposed. First, sine chaos mapping was used to initialize the chimpanzee population and enhance the quality and diversity of the initialized population. Then the global search capability and local exploitation capability of the optimization algorithm at iteration are enhanced by improving the convergence factor f and dynamically changing the number of chimpanzee precedence echelons. Finally, 10 benchmark functions are used to test the optimization‐seeking performance of the Improved Chimp Optimization Algorithm, while an engineering design optimization problem is introduced to compare the experimental results with other swarm intelligence optimization algorithms. The Improved Chimp Optimization Algorithm is shown to have better convergence and solution accuracy, resulting in an improvement in the global optimization‐seeking capability of the original Chimp Optimization Algorithm.