Volume 1, Issue 3, June 2013, Page: 53-58
Ant Colony Optimization with Genetic Operations
Matej Ciba, Institute of Control and Industrial Informatics, Bratislava, Slovakia
Ivan Sekaj, Institute of Control and Industrial Informatics, Bratislava, Slovakia
Received: Jun. 12, 2013;       Published: Jun. 30, 2013
DOI: 10.11648/j.acis.20130103.13      View  2899      Downloads  97
This paper attempts to overcome stagnation problem of Ant Colony Optimization (ACO) algorithms. Stagnation is undesirable state which occurs at a later phases of the search process. Excessive pheromone values attract more ants and make further exploration hardly possible. This problem has been addressed by Genetic operations (GO) incorporated into ACO framework. Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. Genetic operations decrease selection pressure and increase probability of finding the global optimum. Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance.
Ant Colony Optimization, Genetic Operations, Crossover, Mutation, Minimal Path Search
To cite this article
Matej Ciba, Ivan Sekaj, Ant Colony Optimization with Genetic Operations, Automation, Control and Intelligent Systems. Vol. 1, No. 3, 2013, pp. 53-58. doi: 10.11648/j.acis.20130103.13
P. E. Hart, N. J. Nilsson and B. Raphael, A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics SSC4 4(2), 1968, 100–107
M. Dorigo, G. Caro and L. Gambardella, Ant algorithms for discrete optimization, Artificial Life, 5(2), 1999, 137-172
L. Gambardella and M. Dorigo, Solving symmetric and asymmetric TSPs by ant colonies, In Proceedings of the IEEE Conference on Evolutionary Computation, ICEC96, IEEE Press, 1996, 622–627
Y. Nakamichi and T. Arita, Diversity control in ant colony optimization, In Abbas HA (ed) Proceedings of the Inaugural Workshop on Artificial Life (AL'01), Adelaide, Australia, Dec 11, 2001, 70-78
R. Kumar M. K. Tiwari and R. Shankar, Scheduling of flexible manufacturing systems: An ant colony optimization approach, proc. Instn. Mech. Engrs Vol. 217 Part B: J. Engineering Manufacture, 2003, 1443–1453
J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press, 1975
I Sekaj, Evolucne vypocty a ich vyuzitie v praxi, IRIS Press, 2005
M. Becker and H. Szczerbicka, Parameters influencing the performance of ant algorithms applied to optimization of buffer size in manufacturing, IEMS Vol. 4, No. 2, December 2005, 184–191
M. Ciba, ACO algorithm with macro cycles, Proceedings on 14th Conference of Doctorial Students on Elitech’12, Slovak Technical University of Bratislava, May 2012
Browse journals by subject