Simulated Annealing Algorithm for a Medium-Sized TSP Data

Abstract

Traveling Salesman Problem (TSP) is among the most popular and widely studied NP-hard problems in the literature. There exist many mathematical models, applications and proposed techniques for TSP. In a classic TSP, the problem consists of dispersed locations in a space; so salesman aims to visit all of the locations constructing the best minimal tour. The problem has great attention by scientists in the field of operations research and other scientific areas since it was put forth. There exists quite a lot exact, heuristic and meta-heuristic technique for TSP. In this study, Simulated Annealing (SA) algorithm has been applied on a group of randomly generated medium-sized TSP problems. Besides, as a neighborhood structure, two well-known operators, which are reverse and swap-reverse, were implemented through SA. At last, the findings and algorithm performance were given by a comparison with operators and randomly generated TSP data.