Study of Variation in TSP using Genetic Algorithm and Its Operator Comparison
Shalini Singh1, Ejaz Aslam Lodhi2
1Shalini Singh, Department of Electronics and Engineering, Indira Gandhi Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India.
2Ejaz Aslam Lodhi,, Department of Electronics and Engineering, Indira Gandhi Institute of Technology, Guru Gobind Singh Indraprastha University, New Delhi, India.
Manuscript received on April 04, 2013. | Revised Manuscript received on April 28, 2013. | Manuscript published on May 05, 2013. | PP: 264-267 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1514053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Purpose of this Paper is to give near optimal solution in terms of quality and computation time. By implementing Genetic Optimization Technique, the effectiveness of the path has been evaluated in terms of fitness function with the parameter such as tour length. In this research work, we see different variation in traveling salesmen problem using Genetic Algorithm Technique. Considering the Limitation of Nearest Neighbor we find that the number of iteration and resulting time complexity can be minimized by using Genetic approach. We also compare the operator of pursued approach which give the best result for finding the shortest path in a shortest time for moving toward the goal. Thus the optimal distance with the tour length is obtained in a more effective way
Keywords: TSP, Fitness Function, Genetic Algorithm, Nearest Neighbour, GA operators.