Advertisement

Multi-Cast Ant Colony System for the Bus Routing Problem

  • Urszula Boryczka
  • Mariusz Boryczka
Part of the Applied Optimization book series (APOP, volume 86)

Abstract

MCACS-BRP, a new Ant Colony Optimization (ACO) based approach to solve the Bus Routing Problem is presented. MCACS is an extension of ACO, where two hierarchically connected casts of ants optimize two different objective functions. In MCACS-BRP, ants collaborate using information about the best results obtained in the particular cast. Experiments with real data from the Municipal Public Transport Union of the Upper Silesian Industrial District (KZK GOP) show that MCACS-BRP is worth further experiments and extensions.

Keywords

Ant colony system Bus routing problem Multi-cast ant colony system Self-organization Cooperation. 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. A. Bauer, B. Bullnheimer, R.F. Hartl, and C. Strauss. An Ant Colony Optimization approach for the single machine total tardiness problem. In Proceedings of the 1999 Congress on Evolutionary Computation,pages 1445–1450, Piscataway, NJ, 1999. IEEE Press.Google Scholar
  2. B. Boffey. Multiobjective routing problems. Top, 3 (2): 167–220, 1995.MathSciNetzbMATHCrossRefGoogle Scholar
  3. E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, 1999.Google Scholar
  4. E. Bonabeau, F. Henaux, S. Guérin, D. Snyers, P. Kuntz, and G. Théraulaz. Routing in telecommunication networks with “Smart” ant—like agents telecommunication applications. In Proceedings of IATA’98, Second Int. Workshop on Intelligent Agents for Telecommunication Applications, Lectures Notes in AI vol. 1437. Springer Verlag, 1998.Google Scholar
  5. U. Boryczka. Ant Colony System and Bus Routing Problem. In Proceedings of CIMCA’99, Vienna, February 1999.Google Scholar
  6. B. Bullnheimer, R. F. Hartl, and C. Strauss. An improved Ant System algorithm for the Vehicle Routing Problem. Technical Report POM-10/97, Institute of Management Science, University of Vienna, 1997a.Google Scholar
  7. B. Bullnheimer, R. F. Hartl, and C. Strauss. A new rank?based version of the Ant System: A computational study. Technical report, Institute of Management Science, University of Vienna, 1997b.Google Scholar
  8. B. Bullnheimer, R. F. Hartl, and C. Strauss. Applying the Ant System to the Vehicle Routing Problem. In S. Martello In I. H. Osman S. Voß and C. Roucairol, editors, Meta?Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pages 109–120, Kluwer Academics, 1998.Google Scholar
  9. B. Bullnheimer and C. Strauss. Tourenplanung mit dem Ant System. Technical report, Instituts für Betriebwirtschaftslehre, Universität Wien, 1996.Google Scholar
  10. J. C. N. Climaco and E. Q. V. Martins. On the determination of the nondominated paths in a multiobjective network problem. In Proceedings of V Symposiöum über Operations Research, pages 255–258, Köln, 1980.Google Scholar
  11. J. C. N. Climaco and E. Q. V. Martins. A bicriterion shortest path algorithm. European Journal of Operational Research, 11: 399–404, 1982.MathSciNetzbMATHCrossRefGoogle Scholar
  12. A. Colorni, M. Dorigo, and V. Maniezzo. Distributed optimization by ant colonies. In F. Vavala and P. Bourgine, editors, Proceedings First Europ. Conference on Artificial Life, pages 134–142, Cambridge, 1991. MIT Press.Google Scholar
  13. A. Colorni, M. Dorigo, V. Maniezzo, and M. Trubian. Ant system for job-shop scheduling. Belgian Journal of Operations Research, Statistics and Computer Science (JORBEL), 34: 39–53, 1994.zbMATHGoogle Scholar
  14. D. Costa and A. Hertz. Ants can colour graphs. Journal of the Operational Research Society, 48: 295–305, 1997.zbMATHGoogle Scholar
  15. J. R. Daduna and A. Wren. Computer-Aided Transit Scheduling. Springer-Verlag, 1988.Google Scholar
  16. M. den Besten, T. Stützle, and M. Dorigo. Scheduling single machines by ants. Technical Report 99–16, IRIDIA, Université Libre de Bruxelles, Belgium, 1999.Google Scholar
  17. T. Galvâo Dias and J. Falcâo e Cunha. Evaluating DSS for operational planning in public transport systems: Ten years of experience with GIST system. In Proceedings of CASPT 2000, Berlin, Germany, June 2000.Google Scholar
  18. G. DiCaro and M. Dorigo. AntNet: A mobile agents approach to adaptive routing. Technical report, IRIDIA, Université Libre de Bruxelles, 1998a.Google Scholar
  19. G. DiCaro and M. Dorigo. AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research (JAIR), 9: 317365, 1998b.Google Scholar
  20. G. DiCaro and M. Dorigo. Extending AntNet for best-effort quality-of-service routing. In Unpublished presentation at ANTS’98 — From Ant Colonies to Artificial Ants: First International Workshop on Ant Colony Optimization, October 15–16, 1998c.Google Scholar
  21. G. DiCaro and M. Dorigo. Two ant colony algorithms for best-effort routing in datagram networks. In Proceedings of the Tenth LASTED International Conference on Parallel and Distributed Computing and Systems (PDCS’98), pages 541–546, IASTED/ACTA Press, 1998d.Google Scholar
  22. M. Dorigo. Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, IT, 1992.Google Scholar
  23. M. Dorigo and G. Di Caro. The ant colony optimization meta—heuristic. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization. McGraw—Hill, London, UK, 1999.Google Scholar
  24. M. Dorigo, G. DiCaro, and L. Gambardella. Ant algorithms for distributed discrete optimization. Artif. Life, 5 (2): 137–172, 1999.CrossRefGoogle Scholar
  25. M. Dorigo and L. M. Gambardella. A study of some properties of Ant—Q. In Proceedings of Fourth International Conference on Parallel Problem Solving from Nature PPSNIVpages 656–665, Berlin, 1996. Springer—Verlag Google Scholar
  26. M. Dorigo and L. M. Gambardella. Ant colonies for the Traveling Salesman Problem. Biosystems, 43: 73–81, 1997a.CrossRefGoogle Scholar
  27. M. Dorigo and L. M. Gambardella. Ant Colony System: A cooperative learning approach to the Traveling Salesman Problem. IEEE Trans. Evol. Comp, 1: 53–66, 1997b.CrossRefGoogle Scholar
  28. M. Dorigo, V. Maniezzo, and A. Colorni. Positive feedback as a search strategy. Technical Report 91–016, Politechnico di Milano, Italy, 1991.Google Scholar
  29. M. Dorigo, V. Maniezzo, and A. Colorni. The Ant System: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man. Cybern, B26: 29–41, 1996.CrossRefGoogle Scholar
  30. O. Engelhardt-Funke and M. Kolonko. Cost—Benefit—Analysis of investments into railway networks with randomly perturbed operations. In Proceedings of CASPT 2000Berlin, Germany, June 2000 Google Scholar
  31. R. Freling. Scheduling Train Crews. A case study for the dutch railways. In Proceedings of CASPT 2000, Berlin, Germany, June 2000.Google Scholar
  32. G. Gallo, G. Longo, S. Nguyen, and S. Pallottino. Directed hypergraphs and applications. Discrete Applied Mathematics, 40: 177–201, 1992.MathSciNetGoogle Scholar
  33. L. M. Gambardella and M. Dorigo. Ant—Q. A reinforcement learning approach to the Traveling Salesman Problem. In Proceedings of Twelfth International Conference on Machine Learningpages 252–260, Palo Alto, CA, 1995. Morgan Kaufman Google Scholar
  34. L. M. Gambardella and M. Dorigo. Solving symmetric and asymmetric TSPs by ant colonies. In Proceedings of the IEEE Conference on Evolutionary Computation ICEC96pages 622–627. IEEE Press, 1996 Google Scholar
  35. L. M. Gambardella and M. Dorigo. HAS—SOP: Hybrid Ant System for the Sequential Ordering Problem. Technical Report 11, IDSIA, 1997.Google Scholar
  36. L. M. Gambardella, E. Taillard, and G. Agazzi. MACS—VRPTW: A Multiple Ant Colony System for vehicle routing problems with time windows. Technical Report 06–99, IDSIA, Lugano, Switzerland, 1999a.Google Scholar
  37. L. M. Gambardella, E. D. Taulard, and M. Dorigo. Ant colonies for the QAP. Technical Report 4–97, IDSIA, Lugano, Switzerland, 1997.Google Scholar
  38. L. M. Gambardella, E. D. Taillard, and M. Dorigo. Ant colonies for the QAP. Journal of the Operational Research Society (JORS), 50 (2): 167–176, 1999b.zbMATHGoogle Scholar
  39. F. Glover and M. Laguna. Tabu Search. Kluwer Academic Publishers, Dordrecht, 1997.zbMATHCrossRefGoogle Scholar
  40. P.-P. Grasse. La reconstruction du nid et les coordinations inter—individuelles chez bellicositermes natalensis et cubitermes sp. La theorie de la stigmerie. Insects Soc, 6: 41–80, 1959.CrossRefGoogle Scholar
  41. R-P. Grasse. Termitologia, volume II. Paris, Masson, 1984.Google Scholar
  42. P. Hansen. Bicriterion path problems. In G. Fandel and T. Gal, editors, Multi-criteria decision making: theory and applications, Lecture Notes in Economics and Mathematical Systems 177, pages 236–245. Springer, Heidelberg, 1980.Google Scholar
  43. M. Heusse, S. Guérin, D. Snyers, and P. Kuntz. Adaptive agent—driven routing and load balancing in communication networks. Technical Report RR98001—IASC, Department Intelligence Artificielle et Sciences Cognitives, ENST Bretagne, 1998.Google Scholar
  44. D. E. Kaufman and R. L. Smith. Fastest paths in time—dependent networks for intelligent vehicle—highway systems applications. IVHS Journal, 1 (1): 1–11, 1993.Google Scholar
  45. A. S. K Kwan, R. S. K. Kwan, M. E. Parker, and A. Wren. Proving the versatility of automatic driver scheduling on difficult train * bus problems. In Proceedings of CASPT 2000, Berlin, Germany, June 2000.Google Scholar
  46. G. Leguizamen and Z. Michalewicz. A new version of Ant System for subset problems. In Proceedings of the 1999 Congress on Evolutionary Computation,pages 1459–1464, Piscataway, NJ, 1999. IEEE Press.Google Scholar
  47. Y.-C. Liang and A. E. Smith. An Ant System approach to redundancy allocation. In Proceedings of the 1999 Congress on Evolutionary Computation,pages 1478–1484, Piscataway, NJ, 1999. IEEE Press.Google Scholar
  48. H. Ramalhinho Lourenço and D. Serra. Adaptive approach heuristics for the generalized assignment problem. Technical Report EWP Series No. 304, Department of Economics and Management, Universitat Pompeu Fabra, Barcelona, 1998.Google Scholar
  49. V. Maniezzo. Exact and approximate nondeterministic tree—search procedures for the quadratic assignment problem. Technical Report CSR 98–1, C. L. In Scienze dell’Informazione, Università di Bologna, sede di Cesena, Italy, 1998.Google Scholar
  50. V. Maniezzo and A. Carbonaro. An ANTS heuristic for the frequency assignment problem. Technical Report CSR 98–4, Scienze dell’Informazione, Università di Bologna, Sede di Cesena, Italy, 1998.Google Scholar
  51. V. Maniezzo and A. Colomi. The Ant System applied to the Quadratic Assignment Problem. IEEE Trans. Knowledge and Data Engineering, 1999.Google Scholar
  52. V. Maniezzo and A. Colorni. An ANTS heuristic for the frequency assignment problem. Future Generation Computer Systems, 16: 927–935, 2000.CrossRefGoogle Scholar
  53. V. Maniezzo, A. Colorni, and M. Dorigo. The Ant System applied to the Quadratic Assignment Problem. Technical Report 94-?-28, IRIDIA, Université Libre de Bruxelles, Belgium, 1994.Google Scholar
  54. E. Q. V. Martins. On a Multicriteria Shortest Path Problem. European Journal of Operational Research, 16: 236–245, 1984.MathSciNetzbMATHCrossRefGoogle Scholar
  55. M. Meilton. Selecting and implementing a computer aided scheduling system for a large bus company. In Proceedings of CASPT 2000, Berlin, Germany, June 2000.Google Scholar
  56. R. Michel and M. Middendorf. An island model based Ant System with lookahead for the Shortest Supersequence Problem. In A. E. Eiben, T. Back, M. Schoenauer, and H.-P. Schwefel, editors, Proceedings of PPSN-V, Fifth International Conference on Parallel Problem Solving from Nature, pages 692–701. Springer-Verlag, 1998.Google Scholar
  57. R. Michel and M. Middendorf. An ACO algorithm for the Shortest Common Supersequence Problem. In D. Come, M. Dorigo, and F. Glover, editors, New Methods in Optimisation. McGraw-Hill, 1999.Google Scholar
  58. J. Mote, I. Murthy, and D. Olson. A parametric approach to solving bicriterion shortest path problems. European Journal of Operational Research, 53: 81–92, 1991.zbMATHCrossRefGoogle Scholar
  59. K. Nachtigall. Time depending shortest-path problems with applications to railway networks. European Journal of Operational Research, 83 (1): 154–166, 1995.zbMATHCrossRefGoogle Scholar
  60. S. Nguyen and S. Pallottino. Equilibrium traffic assignment for large scale transit networks. European Journal of Operational Research, 37: 176–186, 1988.MathSciNetzbMATHCrossRefGoogle Scholar
  61. A. Orda and R. Rom. Shortest-path and minimum-delay algorithms in network with time-dependent edge length. Journal of the ACM, 37 (3): 607–625, 1990.MathSciNetzbMATHGoogle Scholar
  62. A. Orda and R. Rom. Minimum weight paths in time-dependent network. Networks, 21 (3): 295–320, 1991.MathSciNetzbMATHCrossRefGoogle Scholar
  63. I. Osman and G. Laporte. Metaheuristics: A bibliography. Annals of Operations Research, 63: 513–623, 1996.zbMATHCrossRefGoogle Scholar
  64. A. De Palma, P. Hansen, and M. Labbé. Commuters’ paths with penalties for early or late arrival times. Transportation Science, 24 (4): 276–286, 1993.CrossRefGoogle Scholar
  65. C. Reeves. Modem heuristic techniques for combinatorial problems. In Advanced Topics in Computer Science. McGrawHill, London, 1995.Google Scholar
  66. J.-M. Rousseau. Computer Scheduling of Public Transport 2. North Holland, 1985.Google Scholar
  67. J.-M. Rousseau. Scheduling regional transportation with HASTUS. In Proceedings of CASPT 2000, Berlin, Germany, June 2000.Google Scholar
  68. H. M. Safer and J. B. Orlin. Fast approximation schemes for multi-criteria combinatorial optimization. Technical Report 3756–95, Sloan School of Management, Massachusetts Institute of Technology, 1995.Google Scholar
  69. R. Schoonderwoerd, O. Holland, and J. Bruten. Ant-like agents for load balancing in telecommunications networks. In Proceedings of the First International Conference on Autonomous Agents, pages 209–216. ACM Press, 1997.Google Scholar
  70. R. Schoonderwoerd, O. Holland, J. Bruten, and L. Rothkrantz. Ant-based load balancing in telecommunications networks. Adaptive Behavior, 5 (2): 169–207, 1996.CrossRefGoogle Scholar
  71. A. Schrijver. Minimum circulation of railway stock. CWI Quarterly, 6: 205–217, 1993.zbMATHGoogle Scholar
  72. C. R. Delgado Sema and J. Pacheco Bonrostro. MINMAX vehicle routing problems: Application to school transport in the province of Burgos (Spain). In Proceedings of CASPT 2000, Berlin, Germany, June 2000.Google Scholar
  73. T. Stützle. An ant approach to the Flow Shop Problem. Technical Report AIDA-97–07, FG Intellektik, FB Informatik, TH Darmstadt, September 1997.Google Scholar
  74. T. Stützle and H. Hoos. Improvements on the Ant System: Introducing MAX-MIN Ant System. In In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms,pages 245–249, Wien, 1997a. Springer Verlag.Google Scholar
  75. T. Stützle and H. Hoos. The MAX-MIN Ant System and Local Search for the Traveling Salesman Problem. In T. Baeck, Z. Michalewicz, and X. Yao, editors, Proceedings of IEEE-ICEC-EPS’97, IEEE International Conference on Evolutionary Computation and Evolutionary Programming Conference, pages 309–314. IEEE Press, 1997b.Google Scholar
  76. T. Stüzle and H. Hoos. MAX-MIN Ant System and Local Search for combinatorial optimisation problems. In Proceedings of the Second International conference on Metaheuristics MIC’97,Dordrecht, 1998. Kluwer Academic.Google Scholar
  77. D. Subramanian, P. Druschel, and J. Chen. Ants and Reinforcement Learning: A case study in routing in dynamic networks. In Proceedings of IJCAI-97, International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1997.Google Scholar
  78. T. Tuip. CVI: Bilder of Dutch Public Transport Information System. Personal Communication, 1993.Google Scholar
  79. R. van der Put. Routing in the faxfactory using mobile agents. Technical Report R*D—SV-98–276, KPN Research, 1998.Google Scholar
  80. G. Navarro Varela and M. C. Sinclair. Ant Colony Optimisation for virtualwavelength—path routing and wavelength allocation. In Proceedings of the 1999 Congress on Evolutionary Computation,pages 1809–1816, Piscataway, NJ, 1999. IEEE Press.Google Scholar
  81. A. R. Warburton. Bicriterion shortest path problems. Technical Report 84–27, University of Ottawa, 1984.Google Scholar
  82. A. R. Warburton. Approximation of pareto optima in multiple—objective shortest—path problems. Operations Research, 35: 70–79, 1987.MathSciNetzbMATHCrossRefGoogle Scholar
  83. T. White, B. Pagurek, and F. Oppacher. Connection management using adaptive mobile agents. In H.R. Arabnia, editor, Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’98), pages 802–809. CSREA Press, 1998.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Urszula Boryczka
    • 1
  • Mariusz Boryczka
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations