Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved.
[1] J. Kennedy, and R. Eberhart, "Particle swarm optimization,” in IEEE International Conference on Neural Networks, Proceedings, vol. 4, pp. 1942 1948, 1995.
[2] M. Dorigo, G. Di Caro, and L. M. Gambardella, "Ant algorithms for discrete optimization,” Artificial Life, vol. 5, no. 2, pp. 137-172, 1999
[3] M. Dorigo, M. Birattari, et T. Stutzle, "Antcolonyoptimization,” IEEE Computational Intelligence Magazine, vol. 1, no 4, pp. 28‑39, nov. 2006.
[4] 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: Cybernetics, vol. 26, no 1, pp. 29 41, feb. 1996.
[5] M. Dorigo, and L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no 1, pp. 53‑66, avr. 1997.
[6] T. Stutzle, and H. Hoos, "MAX-MIN Ant System and local search for the traveling salesman problem ,”in , IEEE International Conference on Evolutionary Computation, pp. 309-314, 1997.
[7] F. Roces, J. Tautz, and B. Hölldobler, "Stridulation in leaf-cutting ants,” Naturwissenschaften, vol. 80, no 11, pp. 521 524, nov. 1993.
[8] R. Hickling, and R. L. Brown, "Analysis of acoustic communication by ants,” The Journal of the Acoustical Society of America, vol. 108, no 4, pp. 1920-1929, oct.2000.
[9] Markl H., "Stridulation in Leaf-Cutting Ants,” Science, vol. 149, no. 3690, pp. 1392-1393, sep. 1965.
[10] T. Schiavinotto, and T. Stützle, "A review of metrics on permutations for search landscape analysis,”Computers&Operations Research, vol. 34, no 10, pp. 3143‑3153, oct. 2007.
[11] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, and Q. X. Wang, "Particle swarm optimization-based algorithms for TSP and generalized TSP,” Information Processing Letters, vol. 103, no 5, pp. 169‑176, août 2007.
[12] L. M. Gambardella, and M. Dorigo, "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem,” INFORMS Journal on Computing, vol. 12, no 3, pp. 237‑255, août 2000
[13] S. Ouadfel and M. Batouche, "Ant colony system with local search for Markov random field image segmentation,” in 2003 International Conference on Image Processing, 2003. ICIP 2003. Proceedings, 2003, vol. 1, pp. I‑133‑6.
[14] D. J. Rosenkrantz, R. E. Stearns, and P. M. Lewis, II, "An Analysis of Several Heuristics for the Traveling Salesman Problem,” SIAM Journal on Computing, vol. 6, no 3, pp. 563‑581, sep. 1977.
[1] J. Kennedy, and R. Eberhart, "Particle swarm optimization,” in IEEE International Conference on Neural Networks, Proceedings, vol. 4, pp. 1942 1948, 1995.
[2] M. Dorigo, G. Di Caro, and L. M. Gambardella, "Ant algorithms for discrete optimization,” Artificial Life, vol. 5, no. 2, pp. 137-172, 1999
[3] M. Dorigo, M. Birattari, et T. Stutzle, "Antcolonyoptimization,” IEEE Computational Intelligence Magazine, vol. 1, no 4, pp. 28‑39, nov. 2006.
[4] 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: Cybernetics, vol. 26, no 1, pp. 29 41, feb. 1996.
[5] M. Dorigo, and L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no 1, pp. 53‑66, avr. 1997.
[6] T. Stutzle, and H. Hoos, "MAX-MIN Ant System and local search for the traveling salesman problem ,”in , IEEE International Conference on Evolutionary Computation, pp. 309-314, 1997.
[7] F. Roces, J. Tautz, and B. Hölldobler, "Stridulation in leaf-cutting ants,” Naturwissenschaften, vol. 80, no 11, pp. 521 524, nov. 1993.
[8] R. Hickling, and R. L. Brown, "Analysis of acoustic communication by ants,” The Journal of the Acoustical Society of America, vol. 108, no 4, pp. 1920-1929, oct.2000.
[9] Markl H., "Stridulation in Leaf-Cutting Ants,” Science, vol. 149, no. 3690, pp. 1392-1393, sep. 1965.
[10] T. Schiavinotto, and T. Stützle, "A review of metrics on permutations for search landscape analysis,”Computers&Operations Research, vol. 34, no 10, pp. 3143‑3153, oct. 2007.
[11] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, and Q. X. Wang, "Particle swarm optimization-based algorithms for TSP and generalized TSP,” Information Processing Letters, vol. 103, no 5, pp. 169‑176, août 2007.
[12] L. M. Gambardella, and M. Dorigo, "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem,” INFORMS Journal on Computing, vol. 12, no 3, pp. 237‑255, août 2000
[13] S. Ouadfel and M. Batouche, "Ant colony system with local search for Markov random field image segmentation,” in 2003 International Conference on Image Processing, 2003. ICIP 2003. Proceedings, 2003, vol. 1, pp. I‑133‑6.
[14] D. J. Rosenkrantz, R. E. Stearns, and P. M. Lewis, II, "An Analysis of Several Heuristics for the Traveling Salesman Problem,” SIAM Journal on Computing, vol. 6, no 3, pp. 563‑581, sep. 1977.
@article{"International Journal of Information, Control and Computer Sciences:63770", author = "S. Bougrine and S. Ouchraa and B. Ahiod and A. A. El Imrani", title = "Ant System with Acoustic Communication", abstract = "Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This process allows fine and local exploration of search space and permits optimal solution to be improved.
", keywords = "Acoustic Communication, Ant Colony Optimization, Local Search, Traveling Salesman Problem.", volume = "8", number = "4", pages = "594-5", }
