An Optimized Grey Wolf Algorithm

Abstract

An Enhanced Grey Wolf Optimizer (EGWO) is proposed to solve the problem that the population diversity of the grey wolf algorithm decreases in the late iteration and it is easy to fall into local extremum. Firstly, a nonlinear convergence factor of cosine function is proposed to balance global search and local search; secondly, chaotic mapping and reverse learning strategies are integrated to increase population diversity; finally, in order to accelerate the convergence speed of the algorithm, the strategy of piecewise updating the position equation of the grey wolf is used to guide the grey wolf to keep close to the head wolf, guide the evolution of the grey wolf, and improve the convergence speed and accuracy of the algorithm. The simulation results show that the improved grey wolf algorithm has faster convergence speed, higher accuracy and better global and local search ability than the other four grey wolf algorithms.