Optimization Techniques for Solving Travelling Salesman Problem

Abstract

In the traveling salesman problem (TSP) we wish to find a tour of all nodes in a weighted graph so that the total weight is minimized. The traveling salesman problem is NP-hard but has many real world applications so a good solution would be useful. In this paper, we present several modern optimization techniques to find the shortest tour through all cities (nodes). Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria Foraging Optimization (BFO), and Bee Colony Optimization (BCO) are applied on several datasets of TSP with different number of cities and different representation: distances between cities, or coordinates of cities. Each optimization technique has unique behaviors which survives it against other techniques. In this paper, the results and comparative study will present for each dataset to calculate the minimum distance and plat the resultant path.