Adaptive Grey Wolf Optimization Algorithm with Neighborhood Search Operations: An Application for Traveling Salesman Problem

Abstract

Grey wolf optimization (GWO) algorithm is one of the best population-based algorithms. GWO allows sharing information in the wolf population based on the leadership hierarchy using the hunting mechanism behavior of real wolves in nature. However, the algorithm does not represent any key exchange information sharing for the traveling salesman problem because of two issues. The candidate solutions are improved dependently, similar to local search concepts, losing their capability as a population-based algorithm. The algorithm is limited in its search process in finding only the local regions and ignoring any chance to explore search space effectively. This study introduced an adaptive grey wolf optimization algorithm (A-GWO) to solve the information-sharing problem. The proposed AGWO maintains sufficient diverse solutions among the best three wolves and the rest of the population. It also improves its neighborhood search by obtaining more locally explored regions to enhance information sharing among the wolves. An adaptive crossover operator with neighborhood search is proposed to inherit the information between the wolves and provide several neighborhoods to find more solutions in the local region. Experiments are performed on 25 benchmark datasets, and results are compared against 12 state-of-the-art algorithms based on three scenarios.The credibility of the proposed algorithm produces approximately 53%, 58%, and 63% better tour distance in the first,second, and third scenarios, respectively. The proposed A-GWO achieves approximately 87% better minimum tour distance compared with the GWO algorithm.