Improved Whale Optimization Algorithm by Multi-mechanism Fusion

Abstract

To solve the problems of insufficient global exploration ability, low convergence accuracy and slow speed of traditional whale optimization algorithm, an improved whale optimization algorithm by multi-mechanism fusion is proposed. Firstly, the algorithm uses the nonlinear parameter to coordinate the exploration and exploitation ability of the whale optimization algorithm. Secondly, combine with the Harris hawks optimization algorithm, it improves the global exploration and local optimization ability of the whale optimization algorithm. Finally, consider the important role of the fitness of the algorithm in the optimization, the Gaussian detection mechanism is proposed. The improved algorithm and other algorithms are simulated and tested on the eight variable dimension benchmark functions and design problems of tension spring. The results show that the improved whale optimization algorithm by multi-mechanism fusion has better robustness and stability, while ensure convergence accuracy and speed.