Analysis of TSP: Simulated Annealing and Genetic Algorithm Approaches

Abstract

This paper analyzes the performance of the popular heuristic methods 'Simulated Annealing (SA)' and 'Genetic Algorithm (GA)' on the symmetric TSP. TSP is a well-known combinatorial optimization problem in NP-complete class. NP-completeness of TSP originates many specific approximation algorithms to find optimal or near optimal solutions in a reasonable time. On the other hand, both SA and GA are general purpose heuristic methods that are applicable to almost every kind of problem whose solution lies inside a search space. The performance of SA and GA depends on many factors such as the nature of the problem, design of the algorithm, parameter values, etc. In this paper, a GA and an SA algorithm are given and their performance with respect to several factors is analyzed. The algorithms are tested on some benchmark problems (TSPLIB) which are obtainable via Internet from