A fast grey wolf optimization algorithm with exponential iterative search

Aiming at the problems of traditional Grey Wolf Optimization algorithm (GWO), such as easy to fall into local optimization, low precision of optimization, and slow convergence speed, a fast grey wolf optimization algorithm with exponential iterative search is proposed. The tent chaotic mapping method is introduced to realize the diversification of the initial grey wolf population. To balance the global search scope and search speed, an iterative search method for exponential nonlinear control parameters is designed to avoid the algorithm falling into local optimization and accelerate the convergence speed. Finally, an information-sharing method based on the inertia weight coefficient is proposed for the current optimal position of individuals, which dynamically updates the individual step size of grey wolves and improves the speed and accuracy of optimization. Through a variety of standard test functions, compared with the traditional GWO algorithm and three improved algorithms, the performance of the proposed algorithm with fast convergence and strong optimization ability is verified.