Grey wolf optimizer with adaptive large neighborhood search for the traveling salesman problems

In solving the traveling salesman problem (TSP), the grey wolf optimizer (GWO) has demonstrated superior performance; however, it faces challenges in terms of global solution search and computational efficiency, especially as the number of cities increases. This study presents an innovative approach that integrates an adaptive large neighborhood search into GWO. This methodology is designed to expand the solution search space while reducing computational time. Through numerical experiments, we demonstrated that the proposed method significantly reduces computational time and yields superior solutions, particularly for problems involving from 400 to 6,000 cities. These results suggest a significant improvement in the solution of large-scale TSPs, providing more efficient and effective optimization tools for routing problems.