Abstract
This chapter reviews the approaches that have been studied for the online adaptation of the parameters of ant colony optimization (ACO) algorithms, that is, the variation of parameter settings while solving an instance of a problem. We classify these approaches according to the main classes of online parameter-adaptation techniques. One conclusion of this review is that the available approaches do not exploit an in-depth understanding of the effect of individual parameters on the behavior of ACO algorithms. Therefore, this chapter also presents results of an empirical study of the solution quality over computation time for Ant Colony System and MAX-MIN Ant System, two well-known ACO algorithms. The first part of this study provides insights on the behaviour of the algorithms in dependence of fixed parameter settings. One conclusion is that the best fixed parameter settings of MAX-MIN Ant System depend strongly on the available computation time. The second part of the study uses these insights to propose simple, pre-scheduled parameter variations. Our experimental results show that such pre-scheduled parameter variations can dramatically improve the anytime performance of MAX-MIN Ant System.
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References
Amir C., Badr A., Farag I.: A fuzzy logic controller for ant algorithms. Computing and Information Systems 11(2):26–34 (2007)
Anghinolfi D., Boccalatte A., Paolucci M., Vecchiola C.: Performance evaluation of an adaptive ant colony optimization applied to single machine scheduling. In: Li X., et al. (eds.) Simulated Evolution and Learning, 7th International Conference, SEAL 2008, Lecture Notes in Computer Science, vol. 5361, Springer, Heidelberg, Germany, pp. 411–420 (2008)
Battiti R., Brunato M., Mascia F.: Reactive Search and Intelligent Optimization, Operations Research/Computer Science Interfaces, vol. 45. Springer, New York, NY (2008)
Botee H. M., Bonabeau E.: Evolving ant colony optimization. Advances in Complex Systems 1:149–159 (1998)
Cai Z., Huang H., Qin Y., Ma X.: Ant colony optimization based on adaptive volatility rate of pheromone trail. International Journal of Communications, Network and System Sciences 2(8):792–796 (2009)
Chusanapiputt S., Nualhong D., Jantarang S., Phoomvuthisarn S.: Selective self-adaptive approach to ant system for solving unit commitment problem. In: Cattolico M., et al. (eds.) GECCO 2006, ACM press, New York, NY, pp. 1729–1736 (2006)
Colas S., Monmarché N., Gaucher P., Slimane M.: Artificial ants for the optimization of virtual keyboard arrangement for disabled people. In: Monmarché N., et al. (eds.) Artificial Evolution - 8th International Conference, Evolution Artificielle, EA 2007, Lecture Notes in Computer Science, vol. 4926, Springer, Heidelberg, Germany, pp. 87–99 (2008)
Dorigo M.: Ant colony optimization. Scholarpedia 2(3):1461 (2007)
Dorigo M., Di Caro G.: The Ant Colony Optimization meta-heuristic. In: Corne D., Dorigo M., Glover F. (eds.) New Ideas in Optimization, McGraw Hill, London, UK, pp. 11–32 (1999)
Dorigo M., Gambardella L. M.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1):53–66 (1997)
Dorigo M., Stützle T.: Ant Colony Optimization. MIT Press, Cambridge, MA (2004)
Dorigo M., Maniezzo V., Colorni A.: The Ant System: An autocatalytic optimizing process. Tech. Rep. 91-016 Revised, Dipartimento di Elettronica, Politecnico di Milano, Italy (1991)
Dorigo M., Maniezzo V., Colorni A.: Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics - Part B 26(1):29–41 (1996)
Dorigo M., Di Caro G., Gambardella L. M.: Ant algorithms for discrete optimization. Artificial Life 5(2):137–172 (1999)
Dorigo M., Birattari M., Stützle T.: Ant colony optimization: Artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine 1(4):28–39 (2006)
Dorigo M., et al. (eds.): Ant Algorithms: Third International Workshop, ANTS 2002, Lecture Notes in Computer Science, vol. 2463. Springer, Heidelberg, Germany (2002)
Eiben A. E., Michalewicz Z., Schoenauer M., Smith J. E.: Parameter control in evolutionary algorithms. In: [31], pp. 19–46 (2007)
Favaretto D., Moretti E., Pellegrini P.: On the explorative behavior of MAX–MIN Ant System. In: Stützle T., Birattari M., Hoos H. H. (eds.) Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, Lecture Notes in Computer Science, vol. 5752, Springer, Heidelberg, Germany, pp. 115–119 (2009)
Förster M., Bickel B., Hardung B., Kókai G.: Self-adaptive ant colony optimisation applied to function allocation in vehicle networks. In: Thierens D., et al. (eds.) GECCO’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, ACM, New York, NY, pp. 1991–1998 (2007)
Gaertner D., Clark K.: On optimal parameters for ant colony optimization algorithms. In: Arabnia H. R., Joshua R. (eds.) Proceedings of the 2005 International Conference on Artificial Intelligence, ICAI 2005, CSREA Press, pp. 83–89 (2005)
Gambardella L. M., Dorigo M.: Ant-Q: A reinforcement learning approach to the traveling salesman problem. In: Prieditis A., Russell S. (eds.) Proceedings of the Twelfth International Conference on Machine Learning (ML-95), Morgan Kaufmann Publishers, Palo Alto, CA, pp. 252–260 (1995)
Garro B. A., Sossa H., Vazquez R. A.: Evolving ant colony system for optimizing path planning in mobile robots. In: Electronics, Robotics and Automotive Mechanics Conference, IEEE Computer Society, Los Alamitos, CA, pp. 444–449 (2007)
Hao Z., Cai R., Huang H.: An adaptive parameter control strategy for ACO. In: Proceedings of the International Conference on Machine Learning and Cybernetics, IEEE Press, pp. 203–206 (2006)
Hao Z., Huang H., Qin Y., Cai R.: An ACO algorithm with adaptive volatility rate of pheromone trail. In: Shi Y., van Albada G. D., Dongarra J., Sloot P. M. A. (eds.) Computational Science – ICCS 2007, 7th International Conference, Proceedings, Part IV, Lecture Notes in Computer Science, vol. 4490, Springer, Heidelberg, Germany, pp. 1167–1170 (2007)
Hoos H. H., Stützle T.: Stochastic Local Search–Foundations and Applications. Morgan Kaufmann Publishers, San Francisco, CA (2005)
Khichane M., Albert P., Solnon C.: An ACO-based reactive framework for ant colony optimization: First experiments on constraint satisfaction problems. In: Stützle T. (ed.) Learning and Intelligent Optimization, Third International Conference, LION 3, Lecture Notes in Computer Science, vol. 5851, Springer, Heidelberg, Germany, pp. 119–133 (2009)
Kovářík O., Skrbek M.: Ant colony optimization with castes. In: Kurkova-Pohlova V., Koutnik J. (eds.) ICANN’08: Proceedings of the 18th International Conference on Artificial Neural Networks, Part I, Lecture Notes in Computer Science, vol. 5163, Springer, Heidelberg, Germany, pp. 435–442 (2008)
Li Y., Li W.: Adaptive ant colony optimization algorithm based on information entropy: Foundation and application. Fundamenta Informaticae 77(3):229–242 (2007)
Li Z., Wang Y., Yu J., Zhang Y., Li X.: A novel cloud-based fuzzy self-adaptive ant colony system. In: ICNC’08: Proceedings of the 2008 Fourth International Conference on Natural Computation, IEEE Computer Society, Washington, DC, vol. 7, pp. 460–465 (2008)
Ling W., Luo H.: An adaptive parameter control strategy for ant colony optimization. In: CIS’07: Proceedings of the 2007 International Conference on Computational Intelligence and Security, IEEE Computer Society, Washington, DC, pp. 142–146 (2007)
Lobo F., Lima C. F., Michalewicz Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer, Berlin, Germany (2007)
Martens D., Backer M. D., Haesen R., Vanthienen J., Snoeck M., Baesens B.: Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation 11(5):651–665 (2007)
Melo L., Pereira F., Costa E.: MC-ANT: A multi-colony ant algorithm. In: Artificial Evolution - 9th International Conference, Evolution Artificielle, EA 2009, Lecture Notes in Computer Science, vol. 5975, Springer, Heidelberg, Germany, pp. 25–36 (2009)
Merkle D., Middendorf M.: Prospects for dynamic algorithm control: Lessons from the phase structure of ant scheduling algorithms. In: Heckendorn R. B. (ed.) Proceedings of the 2000 Genetic and Evolutionary Computation Conference - Workshop Program. Workshop “The Next Ten Years of Scheduling Research”, Morgan Kaufmann Publishers, San Francisco, CA, pp. 121–126 (2001)
Merkle D., Middendorf M., Schmeck H.: Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation 6(4):333–346 (2002)
Meyer B.: Convergence control in ACO. In: Genetic and Evolutionary Computation Conference (GECCO), Seattle, WA, late-breaking paper available on CD (2004)
Pellegrini P., Favaretto D., Moretti E.: Exploration in stochastic algorithms: An application on MAX–MIN Ant System. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), Studies in Computational Intelligence, vol. 236, Springer, Berlin, Germany, pp. 1–13 (2009)
Pilat M. L., White T.: Using genetic algorithms to optimize ACS-TSP. In: [16], pp. 282–287 (2002)
Randall M.: Near parameter free ant colony optimisation. In: Dorigo M., et al. (eds.) Ant Colony Optimization and Swarm Intelligence: 4th International Workshop, ANTS 2004, Lecture Notes in Computer Science, vol. 3172, Springer, Heidelberg, Germany, pp. 374–381 (2004)
Randall M., Montgomery J.: Candidate set strategies for ant colony optimisation. In: [16], pp. 243–249 (2002)
Stützle T.: ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem. URL http://www.aco-metaheuristic.org/aco-code/ (2002)
Stützle T., Hoos H. H.: MAX–MIN Ant System. Future Generation Computer Systems 16(8):889–914 (2000)
White T., Pagurek B., Oppacher F. Connection management using adaptive mobile agents. In: Arabnia H. R. (ed.) Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’98), CSREA Press, pp. 802–809 (1998)
Zilberstein S.: Using anytime algorithms in intelligent systems. AI Magazine 17(3):73–83 (1996)
Zlochin M., Birattari M., Meuleau N., Dorigo M.: Model-based search for combinatorial optimization: A critical survey. Annals of Operations Research 131(1–4):373–395 (2004)
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Stützle, T. et al. (2011). Parameter Adaptation in Ant Colony Optimization. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds) Autonomous Search. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21434-9_8
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DOI: https://doi.org/10.1007/978-3-642-21434-9_8
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