Abstract: Gray wolf optimization is a new optimization algorithm.Compared with other groups,it has the problems of low convergence speed,unstable and easy to fall into local optimum.According to the structural characteristics of the GWO algorithm,a chaotic gray wolf optimization algorithm based on the adaptive adjustment strategy was proposed.The adaptive adjustment strategy is used to improve the convergence rate of the GWO algorithm.The chaotic local search strategy is used to increase the diversity of the population and avoid search process falling into the local optimal,Finally,six algorithms are used to simulate the algorithm,and the other four algorithms are compared.The experimental results show that the proposed algorithm has obvious advantages in terms of convergence speed,accuracy and stability.

Key words: Gray wolf optimizatio,Adaptive,Chaos,Convergence rate,Local optimum