Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017, Article ID 7430125, 7 pages
https://doi.org/10.1155/2017/7430125
Research Article

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

1Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
2Department of Computer Science, Foundation University, Islamabad, Pakistan
3Arriyadh Community College, King Saud University, Riyadh, Saudi Arabia
4KSA Workers University, El Mansoura, Egypt
5College of Business Administration, King Saud University, Muzahimiyah, Saudi Arabia

Correspondence should be addressed to Yousaf Shad Muhammad; kp.ude.uaq@fusuoy

Received 1 June 2017; Revised 17 July 2017; Accepted 7 August 2017; Published 25 October 2017

Academic Editor: Silvia Conforto

Copyright © 2017 Abid Hussain et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.