Multi-Strategy Hybrid Grey Wolf Optimizer

This paper presents an improved Grey Wolf Optimizer (GWO) algorithm that incorporates multiple strategies to enhance both convergence speed and solution accuracy. These strategies include the integration of a backward learning approach during the population initialization phase, the implementation of a dynamic adjustment mechanism for adaptive factors, the employment of a multi-population strategy to increase population diversity, and the introduction of a local search mechanism to improve solution precision. The efficacy of the proposed algorithm was validated through simulations conducted on ten standard test functions, which demonstrated significant improvements in convergence speed, stability, and global search capability.