Advertisement

Journal of Heuristics

, Volume 16, Issue 6, pp 859–879 | Cite as

Coalition-based metaheuristic: a self-adaptive metaheuristic using reinforcement learning and mimetism

  • David Meignan
  • Abderrafiaa Koukam
  • Jean-Charles Créput
Article

Abstract

We present a self-adaptive and distributed metaheuristic called Coalition-Based Metaheuristic (CBM). This method is based on the Agent Metaheuristic Framework (AMF) and hyper-heuristic approach. In CBM, several agents, grouped in a coalition, concurrently explore the search space of a given problem instance. Each agent modifies a solution with a set of operators. The selection of these operators is determined by heuristic rules dynamically adapted by individual and collective learning mechanisms. The intention of this study is to exploit AMF and hyper-heuristic approaches to conceive an efficient, flexible and modular metaheuristic. AMF provides a generic model of metaheuristic that encourages modularity, and hyper-heuristic approach gives some guidelines to design flexible search methods. The performance of CBM is assessed by computational experiments on the vehicle routing problem.

Combinatorial optimization Metaheuristic Multiagent system Hyper-heuristic 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aydin, M.E.: Metaheuristic agent teams for job shop scheduling problems. In: 3rd International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing, pp. 185–194 (2007) Google Scholar
  2. Aydin, M.E., Fogarty, T.C.: Teams of autonomous agents for job-shop scheduling problems: an experimental study. J. Intell. Manuf. 15, 455–462 (2004) CrossRefGoogle Scholar
  3. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003) CrossRefGoogle Scholar
  4. Burke, E., Hart, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Handbook of Meta-Heuristics, pp. 457–474. Kluwer, Dordrecht (2003) Google Scholar
  5. Burke, E.K., Hyde, M.R., Kendall, G.: Evolving bin packing heuristics with genetic programming. In: Runarsson, T.P., Beyer, H.G., Burke, E., Merelo-Guervos, J.J., Whitley, L.D., Yao, X. (eds.) Parallel Problem Solving from Nature—PPSN IX. Lecture Notes in Computer Science, vol. 4193, pp. 860–869. Springer, Berlin (2006) CrossRefGoogle Scholar
  6. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A classification of hyper-heuristics approaches. Tech. Rep. Computer Science Technical Report No. NOTTCS-TR-SUB-0907061259-5808, School of Computer Science and Information Technology, University of Nottingham (2009) Google Scholar
  7. Christofides, N., Mingozzi, A., Toth, P.: The vehicle routing problem. In: Combinatorial Optimization pp. 315–338. Wiley, New York (1979) Google Scholar
  8. Cordeau, J.F., Laporte, G., Mercier, A.: A unified tabu search heuristic for vehicle routing problems with time windows. J. Oper. Res. Soc. 52, 928–936 (2001) zbMATHCrossRefGoogle Scholar
  9. Cordeau, J.F., Gendreau, M., Hertz, A., Laporte, G., Sormany, J.S.: New heuristics for the vehicle routing problem. In: Logistics Systems: Design and Optimization, pp. 279–297. Springer, Berlin (2005) CrossRefGoogle Scholar
  10. Crainic, T., Toulouse, M.: Parallel strategies for meta-heuristics. In: State-of-the-Art Handbook in Metaheuristics, pp. 475–513. Kluwer, Dordrecht (2003) Google Scholar
  11. Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959) zbMATHCrossRefMathSciNetGoogle Scholar
  12. Dongarra, J.J.: Performance of various computers using standard linear equations software. Tech. Rep. CS-89-85, Computer Science Department, University of Tennessee and Computer Science and Mathematics Division, Oak Ridge National Laboratory (2006) Google Scholar
  13. Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: algorithms, applications and advances. Tech. Rep. IRIDIA-2000-32, IRIDIA (2000) Google Scholar
  14. Gruer, P., Hilaire, V., Koukam, A., Cetnarowicz, K.: A formal framework for multi-agent systems analysis and design. Expert Syst. Appl. 23(4), 349–355 (2002) CrossRefGoogle Scholar
  15. Hansen, P., Mladenović, N.: Variable neighborhood search. In: Handbook of Metaheuristics, pp. 145–184. Kluwer, Dordrecht (2003) Google Scholar
  16. Hinterding, R., Michalewicz, Z., Eiben, A.E.: Adaptation in evolutionary computation: a survey. In: IEEE International Conference on Evolutionary Computation, pp. 65–69 (1997) Google Scholar
  17. Horling, B., Lesser, V.: A survey of multi-agent organizational paradigms. Knowl. Eng. Rev. 19, 281–316 (2005) CrossRefGoogle Scholar
  18. Jedrzejowicz, P., Wierzbowska, I.: Jade-based a-team environment. In: 6th International Conference on Computational Science, pp. 28–31 (2006) Google Scholar
  19. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996) Google Scholar
  20. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks pp. 1942–1948 (1995). http://www.engr.iupui.edu/~shi/Coference/psopap4.html
  21. Lin, S.: Computer solutions of the traveling salesman problem. Bell Syst. Tech. J. 44, 2245–2269 (1965) zbMATHGoogle Scholar
  22. Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search. In: Handbook of Metaheuristics, pp. 321–353. Kluwer, Dordrecht (2003) Google Scholar
  23. Meignan, D., Créput, J.C., Koukam, A.: A coalition-based metaheuristic for the vehicle routing problem. In: IEEE Congress on Evolutionary Computation, pp. 1176–1182, (2008a) Google Scholar
  24. Meignan, D., Créput, J.C., Koukam, A.: An organizational view of metaheuristics. In: Jennings, N.R., Rogers, A., Petcu, A., Ramchurn, S.D. (eds.) First International Workshop on Optimisation in Multi-Agent Systems, AAMAS’08, pp. 77–85 (2008b) Google Scholar
  25. Mester, D., Bräysy, O.: Active guided evolution strategies for large scale vehicle routing problems with time windows. Comput. Oper. Res. 32, 1593–1314 (2005) CrossRefGoogle Scholar
  26. Mester, D., Bräysy, O.: Active-guided evolution strategies for large-scale capacitated vehicle routing problems. Comput. Oper. Res. 34(10), 2964–2975 (2007) zbMATHCrossRefGoogle Scholar
  27. Milano, M., Roli, A.: Magma: a multiagent architecture for metaheuristics. IEEE Trans. Syst. Man Cybern., Part B 34(2), 925–941 (2004) CrossRefGoogle Scholar
  28. Oliver, I.M., Smith, D.J., Holland, J.R.C.: A study of permutation crossover operators on the traveling salesman problem. In: Grefenstette, J.J. (ed.) International Conference on Genetic Algorithms, pp. 224–230, (1987) Google Scholar
  29. Osman, I.H.: Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Ann. Oper. Res. 41(4), 421–451 (1993) zbMATHCrossRefMathSciNetGoogle Scholar
  30. Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intell. Data Analysis 12(1), 3–23 (2008) Google Scholar
  31. Parunak, H.V.D., Brueckner, S., Fleischer, M., Odell, J.: A design taxonomy of multi-agent interactions. Lect. Not. Comput. Sci. 2935(4), 123–137 (2003) Google Scholar
  32. Prins, C.: A simple and effective evolutionary algorithm for the vehicle routing problem. Comput. Oper. Res. 31, 1985–2002 (2004) CrossRefMathSciNetGoogle Scholar
  33. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Trans. Sci. 40, 421–438 (2006) CrossRefGoogle Scholar
  34. Sutton, R.S., Barto, A.G.: Reinforcement learning: Introduction. Tech. rep., Cognitive Science Research Group (1998) Google Scholar
  35. Taillard, E.D., Gambardella, L.M., Gendreau, M., Potvin, J.Y.: Adaptive memory programming: A unified view of metaheuristics. Eur. J. Oper. Res. 135, 1–16 (2001) zbMATHCrossRefMathSciNetGoogle Scholar
  36. Talbi, E.G., Bachelet, V.: Cosearch: a parallel co-evolutionary metaheuristic. In: Int. Workshop on Hybrid Metaheuritics, pp. 127–140, (2004) Google Scholar
  37. Toth, P., Vigo, D.: The granular tabu search and its application to the vehicle routing problem. INFORMS J. Comput. 15, 333–348 (2003) CrossRefMathSciNetGoogle Scholar
  38. Voss, S.: Meta-heuristics: the state of the art. In: Local Search for Planning and Scheduling. LNCS, vol. 2148, pp. 1–23 (2001) Google Scholar
  39. Wren, A., Holliday, A.: Computer scheduling of vehicles from one or more depots to a number of delivery points. Oper. Res. Q 23(3), 333–344 (1972) CrossRefGoogle Scholar
  40. Yamaguchi, T., Tanaka, Y., Yachida, M.: Speed up reinforcement learning between two agents with adaptive mimetism. In: IEEE International Conference on Intelligent Robots and Systems, vol. 2, pp. 594–600 (1997) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • David Meignan
    • 1
  • Abderrafiaa Koukam
    • 1
  • Jean-Charles Créput
    • 1
  1. 1.Laboratoire Systèmes et TransportsUniversité de Technologie de Belfort-MontbéliardBelfortFrance

Personalised recommendations