Abstract
Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has empirically shown its effectiveness in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). Still, very little theory is available to explain the reasons underlying ACO’s success. An ACO alternative called Omicron ACO (OA), first designed as an analytical tool, is presented. This OA is used to explain the reasons of elitist ACO’s success in the TSP, given a globally convex structure of its solution space.
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References
Birattari, M., Di Caro, G., Dorigo, M.: For a Formal Foundation of the Ant Programming Approach to Combinatorial Optimization. Part 1: The problem, the representation, and the general solution strategy. Technical Report TR-H-301, ATR-Human Information Processing Labs, Kyoto, Japan (2000)
Boese, K.D.: Cost Versus Distance in the Traveling Salesman Problem. Technical Report 950018, Univ. of California, Los Angeles, Computer Science (May 19, 1995)
Dorigo, M., Di Caro, G.: The Ant Colony Optimization Meta-Heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)
Dorigo, M., Stützle, T.: An Experimental Study of the Simple Ant Colony Optimization Algorithm. In: 2001 WSES International Conference on Evolutionary Computation (EC 2001), WSES-Press International (2001)
Guntsch, M., Middendorf, M.: A Population Based Approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 71–80. Springer, Heidelberg (2002)
Guntsch, M., Middendorf, M.: Applying Population Based ACO to Dynamic Optimization Problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)
Gutjahr, W.J.: A graph-based Ant System and its convergence. Future Generation Computer Systems 16(8), 873–888 (2000)
Gutjahr, W.J.: ACO Algorithms with Guaranteed Convergence to the Optimal Solution. Information Processing Letters 82(3), 145–153 (2002)
Hu, T.C., Klee, V., Larman, D.: Optimization of globally convex functions. SIAM Journal on Control and Optimization 27(5), 1026–1047 (1989)
Meuleau, N., Dorigo, M.: Ant colony optimization and stochastic gradient descent. Artificial Life 8(2), 103–121 (2002)
Stützle, T., Dorigo, M.: A Short Convergence Proof for a Class of Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation 6, 358–365 (2002)
Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)
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© 2004 Springer-Verlag Berlin Heidelberg
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Gómez, O., Barán, B. (2004). Reasons of ACO’s Success in TSP. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2004. Lecture Notes in Computer Science, vol 3172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_20
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DOI: https://doi.org/10.1007/978-3-540-28646-2_20
Publisher Name: Springer, Berlin, Heidelberg
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