A grey wolf optimization algorithm with improved nonlinear convergence

According to the premature convergence and easy to fall into local optimum of grey wolf optimization algorithm in solving complex optimization problems, a improved grey wolf optimization algorithm is proposed. The algorithm first uses the reverse learning method introducing the chaos mapping strategy to the initial population, and lays the foundation for the global search. Considering the balance global and the local search capability, the improved nonlinear function mode is proposed for the decrease of the convergence factor. In order to avoid the local optimal algorithm, the Cauchy mutation operation is carried out for the optimal location of the optimal gray wolf. The concrete implementation steps are given, and 8 standard test functions are simulated. The experimental results show that the improved grey wolf optimization algorithm has better accuracy and stability.