Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

The Grey Wolf Optimization Algorithm (GWO) replicates the leadership and foraging mechanisms of the natural grey wolf and excels in solving problems in a variety of domains. However, the algorithm tends to converge to a local optimal and has a slow convergence rate. This paper proposes an enhanced Grey Wolf optimization algorithm (HTGWO) based on hyperbolic tangent inertia weights to solve this problem. HTGWO employs inertia weights based on hyperbolic tangent functions to balance GWO’s global and local search capabilities of the GWO. HTGWO has a faster convergence rate and more accurate solutions than GWO. Five classical test functions were used to construct comparative experiments between the HWGWO and five classical intelligent optimization algorithms. The comparison results indicate that HWGWO has superior convergence speed, solution precision, and stability compared with the other five classical intelligent optimization algorithms. Moreover, experimental evidence suggests that the HWGWO is more effective at solving multimodal functions than unimodal functions. In addition, HTGWO can balance exploration and exploitation by adjusting the parameters according to the characteristics of different problems.