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Ant Algorithms: Theory and Applications

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Abstract

This paper reviews the theory and applications of ant algorithms, new methods of discrete optimization based on the simulation of self-organized colony of biologic ants. The colony can be regarded as a multi-agent system where each agent is functioning independently by simple rules. Unlike the nearly primitive behavior of the agents, the behavior of the whole system happens to be amazingly reasonable. The ant algorithms have been extensively studied by European researchers from the mid-1990s. These algorithms have successfully been applied to solving many complex combinatorial optimization problems, such as the traveling salesman problem, the vehicle routing problem, the problem of graph coloring, the quadratic assignment problem, the problem of network-traffic optimization, the job-shop scheduling problem, etc. The ant algorithms are especially efficient for online optimization of processes in distributed nonstationary systems (for example, telecommunication network routing).

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REFERENCES

  1. Zakharov, A.A., Muravei, semya, koloniya (Ant, Family, Colony), Moscow: Nauka, 1978.

    Google Scholar 

  2. Dorigo, M., Optimization, Learning, and Natural Algorithms, PhD Thesis, Dipartimento di Elettronica, Politechnico di Milano (Italy), 1992.

    Google Scholar 

  3. Dorigo, M., Maniezzo, V., and Colorni, A., The Ant System: Optimization by a Colony of Cooperating Agents, IEEE Trans. Systems, Man Cybernetics, Part B, 1996, vol. 26, no.1, pp. 29–41.

    Google Scholar 

  4. Levanova, T.V. and Loresh, M.A., Ant Colony Algorithm and Simulated Annealing for the Problem of p-Median, Avtom. Telemekh., 2004, no. 3, pp. 80–88.

  5. Shtovba, S.D., Ant Algorithms, Exponenta Pro. Matematika v prilozheniyakh, 2003, no. 4, pp. 70–75.

  6. Bonavear, E. and Dorigo, M., Swarm Intelligence: from Natural to Artificial Systems, Oxford Univ. Press, 1999.

  7. Dorigo, M., Swarm Intelligence, Ant Algorithms and Ant Colony Optimization, Reader for CEU Summer University Course “Complex System,” Budapest: Central European University, 2001, pp. 1–38.

    Google Scholar 

  8. Shvetsova, N., Evolutionary Biology and High Technologies: Future Symbiosis, http://www.cnews.ru/newcom/index.shtml?2002/09/27/136108.

  9. Goss, S., Aron S., Deneubourg J.L., and Pasteels, J.M., Self-Organized Shortcuts in the Argentine Ant, Naturwissenschaften, 1989, no. 76, pp. 579–581.

  10. TSPLIB, http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/.

  11. Gen, M. and Cheng, R., Genetic Algorithms and Engineering Design, Wiley, 1997.

  12. Bullnheimer, B., Hartl, R.F., and Strauss, C., A New Rank-Based Version of the Ant System: A Computational Study, Cent. Eur. J. Oper. Res. Econ., 1999, vol. 7, no.1, pp. 25–38.

    Google Scholar 

  13. Dorigo, M., and Gambardella, L.M., Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Trans. Evolutionary Computation, 1997, vol. 1, no.1, pp. 53–66.

    Article  Google Scholar 

  14. Stutzle, T., and Hoos, H.H., MAX-MIN Ant System, Fut. Generation Comput. Syst., 2000, vol. 16, no.8, pp. 889–914.

    Article  Google Scholar 

  15. Cordon, O., Fernandez de Viana, I., and Moreno, L., A New AGO Model Integrating Evolutionary Concepts: The Best-Worst Ant System, Proc. of ANTS2000—From Ant Colonies to Artificial Ants: A Series of Int. Workshops on Ant Algorithms, Brussels, 2000, pp. 22–29.

  16. Cordon, O., Fernandez de Viana, I., and Herrera, F., Analysis of the Best-Worst Ant Systems and Its Variants on the QAP, Lecture Notes in Computer Science (Proc. III Int. Workshop on Ant Algorithms ANTS 2002), Berlin: Springer, 2002, no. 2463, pp. 228–234.

    Google Scholar 

  17. Reinelt, G., The Traveling Salesman: Computational Solutions for TSP Applications, Lecture Notes in Computer Science, Berlin: Springer, 1994, vol. 840.

    Google Scholar 

  18. Gambardella, L.M. and Dorigo, M., Solving Symmetric and Asymmetric TSPs by Ant Colonies, Proc. IEEE Conf. on Evolutionary Computation—ICEC96, Piscataway, USA, 1996, pp. 622–627.

  19. Reimann, M., Shtovba, S., and Nepomuceno, E., A Hybrid Ant Colony Optimization and Genetic Algorithm Approach for Vehicle Routing Problems Solving, Student Papers of Complex Systems Summer School-2001, Budapest, 2001, pp. 134–141.

  20. Pilat, M. and White, T., Using Genetic Algorithm to Optimize ACS-TSP, Lecture Notes in Computer Science (Proc. III Int. Workshop on Ant Algorithms ANTS 2002), Berlin: Springer, 2002, no. 2463, pp. 282–287.

    Google Scholar 

  21. Acan, A., GAACO: A GA + ACO Hybrid for Faster and Better Search Capability, Lecture Notes in Computer Science (Proc. III Int. Workshop on Ant Algorithms ANTS 2002), Berlin: Springer, 2002, no. 2463, pp. 300–301.

    Google Scholar 

  22. Lucic, P., Modeling Transportation Problems Using Concepts of Swarm Intelligence and Soft Computing, PhD Thesis, Civil Engineering Department, Virginia Polytechnic Institute and State University, Virginia, USA, 2002.

    Google Scholar 

  23. Reimann, M., Ant Based Optimization in Good Transportation, PhD Thesis, University of Vienna, Vienna, Austria, 2002.

    Google Scholar 

  24. Gutiahr, W.J., A Converging ACO Algorithm for Stochastic Combinatorial Optimization, Lecture Notes in Computer Science (Proc. of SAFA-2003 (Stochastic Algorithms: Foundations and Applications)), Berlin: Springer, 2003, no. 2827, pp. 10–25.

    Google Scholar 

  25. Mariano, C.E. and Morales, E., MOAQ: An Ant-Q Algorithm for Multiple Objective Optimization Problems, Proc. of Genetic and Evolutionary Computation Conf. (GECCO-99), San-Francisco, 1999, vol. 1, pp. 894–901.

    Google Scholar 

  26. Maier, H.R., Simpson, A.R., Zecchin, A.C., Wai Kuan Foong, Kuang Yeow Phang, Hsin Yeow Seah, and Chan Lim Tan, Ant Colony Optimization for Design of Water Distribution Systems. J. Water Resources Planning Manag., vol. 129, no.3, pp. 200–209.

  27. Hussian Aziz Saleh, Ants Can Successfully Design GPS Surveying Networks, http://www.gpsworld.com/gpsworld/article/articleDetail.jsp?id=30690&pa geID=1&sk=&date=. Translated under the title Povedenie murav’ev mozhno uspeshno ispol’zovat’ dlya razrabotki s’emochnykh setei GPS optimal’noi struktury, http://www.agp.ru/projects/ants/.

  28. Liang Yun-Chia and Smith, A.E., An Ant System Approach to Redundancy Allocation, Proc. Cong. Evolutionary Computation (CEC-99), 1999, vol. 2.

  29. Eggers, J., Feillet, D., Kehl, S., Wagner, M.O., and Yannou, B., Optimization of the Keyboard Arrangement Problem Using an Ant Colony Algorithm, Eur. J. Operational Res., 2003, no. 148, pp. 672–686.

  30. Rodrigues, A., Application of Ant Colony Optimization to Data Distribution in Memory in Computer Systems, Proc. VII Annual Swarm Researchers Meeting “Swarm-Fest-2003,” Notre Dame, USA, 2003, http://www.nd.edu/arodrig6/.

  31. Rajesh, J., Gupta, K., Kusumakar, H.S., Jayaraman, V.K., and Kulkarni, B.D., Dynamic Optimization of Chemical Processes Using Ant Colony Framework, Comput. Chem., 2001, vol. 25, no.6, pp. 583–595.

    Article  PubMed  Google Scholar 

  32. Socha, K., Knowles, J., and Samples, M., A MAX-MIN Ant System for the University Course Timetabling Problem, Lecture Notes in Computer Science (Proc. III Int. Workshop on Ant Algorithms ANTS 2002), Berlin: Springer, 2002, no. 2463, pp. 1–13.

    Google Scholar 

  33. De Jong, J., Multiple Ant Colony Systems for the Busstop Allocation Problem, MS Thesis, Department of Philology, University of Utrecht, Utrecht, Holland, 2001.

    Google Scholar 

  34. Shmygelska, A. and Hoos, H., An Improved Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem, Proc. XVI Canadian Conf. Artificial Intelligence (AI-2003), Canada, 2003.

  35. Gueret, C., Monmarche, N., and Slimane, M., Ants Can Play Music, Lecture Notes in Computer Science (Proc. of IV Int. Workshop on Ant Colony Optimization and Swarm Intelligence ANTS-2004), Berlin: Springer, 2004, no. 3172, pp. 310–317.

    Google Scholar 

  36. Aupetit, S., Bordeau, V., Monmarche, N., Slimane, M., and Venturini, G., Interactive Evolution of Ant Paintings, Proc. of IEEE Cong. on Evolutionary Computation, Canberra: IEEE Press, 2003, pp. 1376–1383.

    Google Scholar 

  37. De Campos, L.M., Gamez, J.A., and Puerta, J.M., Learning Bayesian Networks by Ant Colony Optimization: Searching in Two Different Spaces, Mathware & Soft Computing, 2002, no. 9.

  38. Raspinelli, J.M., Lopes, H.S., and Freitas, A.A., Data Mining with an Ant Colony Optimization Algorithm, IEEE Trans. Evolutionary Computation (Special issue on Ant Colony Algorithms), 2002, vol. 6, no.4, pp. 321–332.

    Google Scholar 

  39. Cassillas, J., Cordon, O., and Herrera, F., Learning Fuzzy Rules Using Ant Colony Optimization Algorithms, Proc. of ANTS2000—From Ant Colonies to Artificial Ants: A Series of Int. Workshops on Ant Algorithms, Brussels, 2000, pp. 13–21.

  40. Cassillas, J., Cordon, O., Fernandez de Viana, I., and Herrera, F., Learning Cooperative Linguistic Fuzzy Rules Using the Best-Worst Ant System Algorithm, Int. J. Intelligent Sys., 2005, vol. 20, pp. 433–452.

    Article  Google Scholar 

  41. Component Transport Follows the Ant Trail, http://www.siemens.com. September 23, 2004.

  42. Caro, G.D. and Dorigo, M., Anet: A Mobile Agents Approach to Adaptive Routing, Tech. Rep. IRIDA 97-12, Brussels: Univ. Libre de Brusseles, 1997.

    Google Scholar 

  43. Dorigo, M. and Stutzle, T., The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances, in Handbook of Metaheuristics, Glover, F. and Kochenberger, G., Eds., Norwell: Kluwer, 2002.

    Google Scholar 

  44. Cordon, O., Herrera, F., and Stutzle, T., A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends, Mathware & Soft Computing, 2002, no. 9.

  45. Dorigo, M. and Stutzle, T., Ant Colony Optimization, Bradford Book, 2004.

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Translated from Programmirovanie, Vol. 31, No. 4, 2005.

Original Russian Text Copyright © 2005 by Shtovba.

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Shtovba, S.D. Ant Algorithms: Theory and Applications. Program Comput Soft 31, 167–178 (2005). https://doi.org/10.1007/s11086-005-0029-1

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