Ant Colony Optimization: A Tutorial Review

Authors:

Sapna Katiyar (Jamia Millia Islamia, New Delhi)

Ibraheem (Jamia Millia Islamia, New Delhi)

Abdul Quaiyum Ansari (Corresponding Author, IEEE)

Keywords: Hybridization, Metaheuristic, Parameters optimization, PseudoRandom-Proportional Action Choice Rule, Pheromone.

Abstract:

The complex social behaviors of ants have been much studied, and now scientists are finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find shortest paths, has become the field of ant colony optimization (ACO). Ant Colony Optimization (ACO) is a derivative of Swarm intelligence (SI). The ant colony optimization algorithm (ACO), introduced by Marco Dorigo, in the year 1992 and it is a paradigm for designing meta heuristic algorithms for optimization problems and is inspired by the foraging behavior of ant colonies. Ant Colony Optimization targets discrete optimization problems and can be extended to continuous optimization problems which is useful to find approximate solutions. Now-a-days, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to solve difficult discrete optimization problems. In fact, ACO algorithm is the most successful and widely recognized algorithm based on the ant behavior. This paper gives an overview of growing research field from theoretical inception to the practical applications of ACO variants and some of the fields where it can be applied.

References:

[1] M. Dorigo. Optimization, Learning and Natural Algorithms, Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992.

[2] M. Dorigo, & L. M. Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, 1(1), 53-66, 1997.

[3] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant System: Optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics – Part B, vol. 26, no. 1, pp. 29–41, 1996.

[4] M. Dorigo and G. Di Caro, “The Ant Colony Optimization Meta-heuristic”, in New Ideas in Optimization, D. Corne et al., Eds., McGraw Hill, London, UK, pp. 11-32, 1999.

[5] Maniezzo V, Colorni A. “The Ant System applied to the quadratic assignment problem”, IEEE Trans. Data Knowledge Engineering, 11(5), 769–78, 1999.

[6] M. Dorigo, G. Di Caro, and L. M. Gambardella, “Ant algorithms for discrete optimization,” Artificial Life, Vol. 5, No. 2, pp. 137–172, 1999.

[7] M. Dorigo and T. St¨utzle, Ant Colony Optimization, MIT Press, Cambridge, MA, 2004.

[8] A. Q. Ansari, “The Basics of Fuzzy logic: A Tutorial Review,” Computer Education – Stafford – Computer Education Group, U.K., No. 88, pp. 5 - 9, February 1998.

[9] A. Q. Ansari, “Multiple Valued Logic Versus Binary Logic,” CSI Communications, India, Vol. 20, No. 5, pp. 30 - 31, November 1996.

[10] A. Q. Ansari, Moinuddin, S. Deshpande, “Fuzzy Logic Control of Safety and Security Systems,” The Indian Police Journal, Vol. 45, No. 4, pp.56 – 62, October–December 1998.

[11] S. A. Siddiqui, A. Q Ansari, Shikha Agarwal, “A Journey through Fuzzy Philosophy,” Pranjana, India, Vol. 6, No. 2, pp. 29 – 33, July – Dec. 2003.

[12] V. Kumar, A. Q. Ansari, T. Patki, A.B. Patki, S. Joshi, S. Chowdhary, “Applications of Evidence Based Software MR International Journal of Engineering and Technology, Vol. 7, No. 2, December 2015 41 Engineering for I T Systems,” Review of Business and Technology Research, Vol. 1, No. 1, pp. 1 – 7, 2008.

[13] Abdul Quaiyum Ansari, Hierarchical Fuzzy Control for Industrial Automation, Scholar’s Press., Germany, 07/2013; ISBN: 978-3639515923.

[14] Mohammad Ayoub Khan, Abdul Quaiyum Ansari, Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions, IGI Global, USA, 2012; ISBN13: 9781466602946, ISBN10: 1466602945, EISBN13: 9781466602953.

[15] Abdul Quaiyum Ansari, Mohammad Ayoub Khan, ”Fundamentals of Industrial Informatics and Communication Technologies,” in Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions, chapter 1, pages 1-19; IGI Global, USA, 2012; ISBN13: 9781466602946, ISBN10: 1466602945.

[16] Sapna Katiyar, Avneesh Mittal, A. Q. Ansari, T. K. Saxena, “Ant Colony Algorithm Based Adaptive PID Temperature Controller,” Proc. 7th Int. Conf. on Trends in Industrial Measurements and Automation (TIMA 2011), CSIR, Chennai, January 2011.