Grey Wolf algorithm based on S-function and particle swarm optimization
ChenYang Liu, Yongli Wang · Journal of Physics Conference Series · 2020
Abstract Based on the analysis of the shortcomings of the grey wolf optimization algorithm, an improved grey wolf optimization algorithm (SGWO) is proposed. The algorithm uses the convergence factor based on S-function change to balance the global search and local search ability of the algorithm. At the same time, the proportion weight based on Euclidean distance of step size and the individual optimal position of the particle swarm optimization algorithm are introduced to update the grey wolf position, thus speeding up the convergence speed of the algorithm to 8. The simulation results of three classical test functions show that the SGWO algorithm has higher accuracy and better stability.Introduction