A Hybrid Genetic-Ant Colony Algorithm for Travelling Salesman Problem
Abstract
Travelling salesman problem is a well-known problem in optimization algorithms. In this study, we propose a hybrid genetic-ant colony algorithm to solve this problem. There are no certain formulas to determine the parameters of ant colony algorithm. Usually, programmers use the trial and error method to find best values. We use the genetic algorithm to optimize best parameter values of ant colony algorithm. In this way, the success rate of ant colony algorithm is maximized.
Keywords
References
- S. Koziel and X.-S. Yang, Computational optimization, methods and algorithms, vol. 356. Springer, 2011.
- M. Mitchell, An Introduction to genetic algorithms. MIT Press, 1998.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.
- M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, 2006.
- A. Mucherino, O. Seref, O. Seref, O. E. Kundakcioglu, and P. Pardalos, “Monkey search: a novel metaheuristic search for global optimization,” in AIP conference proceedings, 2007, vol. 953, no. 1, pp. 162–173.
- C. Yang, X. Tu, and J. Chen, “Algorithm of marriage in honey bees optimization based on the wolf pack search,” Proc. 2007 Int. Conf. Intell. Pervasive Comput. IPC 2007, pp. 462–467, 2007.
- X.-S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, 2009, pp. 210–214.
- W. Pan, “Knowledge-Based Systems A new Fruit Fly Optimization Algorithm : Taking the financial distress model as an example,” Knowledge-Based Syst., vol. 26, pp. 69–74, 2012.
Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Publication Date
September 30, 2017
Submission Date
July 10, 2017
Acceptance Date
September 25, 2017
Published in Issue
Year 2017 Volume: 1 Number: 3