Travelling Sales Person Problem Using Soft Computing – Genetic Algorithm Techniques

Genetic Algorithms (GAs) is a predominant heuristic technique used to improve the solution space for Travelling Salesman Problem (TSP) and real-time problems in dynamic environments. In this paper various genetic algorithms for TSP are reviewed and deliberated about the statement of a salesman who tries to find out a shortest path from his initial location. The Salesman uses a concept of Hamiltonian cycle (which starts and ends at the same location). Many algorithms are used to solve TSP, like Ant-System (AS) Algorithm from Ant Colony Optimization (ACO), NP-Hard, and Dynamic Programming (DP). ACO techniques to solve the Salesman problems or difficulties faced during travelling to various cities, were reviewed to solve optimally. AS is atechnique of ACO inspired from the behaviour of ants. The concept of soft computing leads to enhance the ACO using GA. The principle is to observe the movement of ants from their nest to the food location by finding the shortest path. This randomized search opens various routes from their nest to food location. An algorithm is presented for the TSP using GA’s concepts after a deep study.