A review of the optimization algorithms on traveling salesman problem

Abstract

The Traveling Salesman Problem (TSP) is arguably the most prominent problem in combinatorial optimization. The simple way in which the problem is defined in combination with its notorious difficulty has stimulated many efforts to find an efficient solution procedure. The TSP is a classic tour problem in which a hypothetical salesman must find the most efficient sequence of destinations in his territory, stopping only once at each, and ending up at the initial starting location. Due to the combinatorial complexity of the TSP, approximate or heuristic solution procedures are almost always employed in practice. Few prospective applications of TSP includes ruling an optimized scan chains route in integrated chip testing, parcels collection and sending in logistics companies, and transportation routing problem. There have been many algorithms introduced to grant time competent solutions for the problem, both exact and approximate. This paper is a review of the recent research work done on various algorithm like genetic algorithm, tabu search algorithm, ant colony algorithm available with respective attributes to find the nearest optimal solution for the traveling salesman problem. It also relates the traveling salesman problem with the available algorithms and provides the advantages in providing a solution for TSP.