Solving Traveling Salesman Problem through Optimization Techniques Using Genetic Algorithm and Ant Colony Optimization

Swarm robotic is a new research area in the domain of Artificial intelligence. Particularly, the swarm robot concept is adopted from Mother Nature that combines small robots in a group to solve a particular problem. This work presents decentralization of swarm robots along-with their methods of optimization, development, applications and implementation in real life domain. It also solves the traveling salesman problem using free parameters; i-e, number of cities, number of iteration and number of ants involved in search within solution space. Furthermore, it compares ant colony optimization with genetic algorithm keeping in mind free parameters. Due to metaheuristic approach Ant colony optimization algorithm perform well and deliver global optimal solution for solving traveling salesman problem relate to genetic algorithm. On other hand, the running time of genetic algorithm and ant colony optimization calculated in two scenarios a. altering number of iteration, b. altering the number of cites to nodes in solution space as input and keeping other parameters constant.