Grey Wolf Optimizer with Multi-Strategy Optimization and Its Application on TSP

Abstract

The Grey Wolf Optimizer (GWO) is an optimized search method inspired by the grey wolf predation activity in the wolf pack. Due to the shortcomings of GWO that are easy to fall into the local minimum and slow convergence speed, a GWO with Multi-Strategy Optimization (MSO-GWO) is proposed, that is, the opposition-based learning strategy method is used in the population initialization stage and the weighted distance is used in the search stage to update the position, so to improve the population diversity and avoid falling into the local minimum. After testing and comparative analysis of various international benchmark functions, the results show that the MSO-GWO algorithm has obvious convergence speed and conversion accuracy. Finally, the MSO-GWO algorithm is used to solve Traveling Salesman Problem (TSP), and its solution effect is remarkable.