International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1002 Swarm Intelligence Technique ACO and Traveling Salesman Problem Harsh Bhalani 1 , Dr. Seema Mahajan 2 , Prof. Zalak Vyas 3 1 Student, Dept. of Computer Engineering, Indus University, Gujarat, India. 2 Head of the Dept. , Dept. of Computer Engineering, Indus University, Gujarat, India. 3 Assistant Professor , Dept. of Computer Engineering, Indus University, Gujarat, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – A swarm is a large number of homogenous, simple agents interacting locally among themselves, and their environment. Swarm Intelligence (SI) can be defined as a relatively new branch of Artificial Intelligence that is used to model the collective behavior of social swarms in nature. The inspiration often comes from nature, especially biological systems. The social interactions among swarm individuals can be either direct or indirect. Examples of direct interaction are through visual or audio contact, such as the waggle dance of honey bees. Indirect interaction occurs when one individual changes the environment and the other individuals respond to the new environment, such as the pheromone trails of ants that they deposit on their way to search for food sources. Examples in natural system SI include bacterial growth, ant colonies, bird flocking, and microbiological intelligence. This paper comprises a snapshot of ant colony optimization algorithm with its application in Traveling Salesman problem (TSP). Key Words: Swarm Intelligence (SI), Artificial Intelligence (AI), Ant Colony Optimization (ACO), and Traveling Salesman Problem (TSP). 1. INTRODUCTION The various techniques of swarm intelligence used by researchers are as follows: 1. Particle Swarm Optimization 2. Ant Colony Optimization 3. Bees Algorithm 4. Artificial Bee Colony Algorithm 5. Differential evolution 6. Artificial Immune System 7. Bat Algorithm 8. Glowworm Swarm Optimization 9. Gravitational Search Algorithm We mainly discuss the Ant Colony Optimization (A.C.O) algorithm in this paper. 1.1 Ant Colony Optimization In 1991, Ant Colony Optimization (ACO) was introduced by M. Dorigo and colleagues for the solution of hard combinatorial optimization (CO) problems. ACO draws inspiration from the social behavior of ant colonies. It is a shown in Fig-1. A. Ants in a pheromone trail between nest and food B. An obstacle interrupts the trail C. Ants find two paths to go around the obstacle D. A new pheromone trail is formed along the shorter path Fig -1: Pattern of path following by Ants 1.1.1 Ant Colony Optimization Metaheuristic As shown in the basic flow of ACO in Fig-2, the objective of ACO’s third step is to construct ant solutions (i.e., find the quality paths on the problem’s construction graphȌ by stochastically moving through neighbor nodes of the graph.