An Improved Wild Horse Optimizer for Traveling Salesman Problem

Abstract

Traveling salesman problem (TSP) is well-known combinatorial optimization problems. Due to its importance in many applications such as engineering sciences, path planning, and sensor placement, many researchers have been attracted to solve this problem. In this paper, a new improved version of Wild horse optimizer (I-WHO) is proposed to boost its performance in solving global optimization and combinatorial optimization problems. To examine the performance of I-WHO, the obtained results are compared with state-of-the-art algorithms. To have an unbiased and accurate comparison, descriptive statistics such as standard deviation, mean, and Wilcoxon rank-sum test are also used. The computational result showed that I-WHO significantly outperforms other alternative algorithms.