Abstract:
Travelling salesman problem being an optimization problem is used to find shortest path of a given set of cities, given a set of cities and the cost of travel (or distance) between each possible pairs, the Travelling salesman problem, is to find the best possible way of visiting all the cities and returning to the starting point that minimize the travel cost (or travel distance). The main objective of this study was to solve travelling salesman problem by using Genetic Algorithm and Ant Colony optimization Algorithms. In this project, we discussed genetic algorithm and ant colony optimization methods and solved travelling salesman problem using these two methods. We applied both techniques to solve the said problem by using the same dataset illustrative examples. MATLAB code was used to find the solution of travelling salesman problem. Comparison was made between the two methods result based on relative error to determine the better one for solving this problem with regards to known optimal value. The result of the comparisons using the illustrative examples considered in this study showed that the genetic algorithm was better method than ant colony optimization. The present investigation can be extended by using different algorithms such as particle swarm optimization and Simulated Annealing and then compared in order to classify which algorithm gives the best optimal results under a given set of conditions.
