Comparison of Particle Swarm Optimization and Simulated Annealing Applied to Travelling Salesman Problem
M. Sumathi1, U. Rahamathunnisa2, A. Anitha3, Druheen Das4, Nallakaruppan. M. K5

1M.Sumathi, Assistant Professor, K Ramakrishnan College of Engineering, Trichy (Tamil Nadu), India.
2U.Rahamathunnisa, Associate, Professor, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3A.Anitha, Associate, Professor, Vellore Instituteof Technology, Vellore (Tamil Nadu), India.
4Druheen Das, MCA Student, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
5Nallakaruppan M.K, Assistant Professor, Vellore Institue of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1578-1583 | Volume-8 Issue-6, April 2019 | Retrieval Number: F4086048619/19©BEIESP
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Abstract: The Travelling Salesman Problem (TSP) is a very old yet very popular and highly researched topic. This problem has numerous solution paths, but to find the path with the least distance, we would have to check the combination of all different paths but that would increase the time to achieve the result. So, in our quest to solve the problem, we try to optimize the algorithms to give the best output that is the shortest path in the least number of iterations. There are various techniques to solve the Travelling Salesman Problem of which we will be using the two most popular techniques that are Particle Swarm Optimization (PSO), and Simulated Annealing (SA). In this paper, we perform a comparative analysis of these two algorithms on the basis of a number of iterations required to reach the optimum path. We have implemented the algorithms in Matlab, and run these algorithms on multiple sets of inputs and various number of inputs in each set. We will be comparing on the basis of number of iterations required for reaching the optimum solution.
Keyword: Travelling Salesman Problem (TSP), Particle Swarm Optimization (PSO), Simulated Annealing (SA).
Scope of the Article: Discrete Optimization