Skip to main content
SpringerLink
Log in

A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems

  • Chapter
Computational Intelligence in Optimization

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 7))

Abstract

This paper is concerned with the development of a novel approach to solve expensive optimisation problems. The approach relies on game theory and a multi-agent framework in which a number of existing algorithms, cast as agents, are deployed with the aim to solve the problem in hand as efficiently as possible. The key factor for the success of this approach is a dynamic resource allocation biased toward promising algorithms on the given problem. This is achieved by allowing the agents to play a cooperative-competitive game the outcomes of which will be used to decide which algorithms, if any, will drop out of the list of solver-agents and which will remain in use. A successful implementation of this framework will result in the most suited algorithm(s) for the given problem being predominantly used on the available computing platform. In other words it guarantees the best use of the resources both algorithms and hardware with the by-product being the best approximate solution for the problem given the available resources. GTMAS is tested on a standard collection of TSP problems. The results are included.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aldea, A., Alcantra, R.B., Jimenez, L., Moreno, A., Martinez, J., Riano, D.: The scope of application of multi-agent systems in the process industry: Three case studies. Expert Systems with Applications 26, 39–47 (2004)

    Article  Google Scholar 

  2. Axelrod, R.: Effective choice in the prisoner’s dilemma. Journal of Conflict Resolution 24(1), 3–25 (1980)

    Google Scholar 

  3. Axelrod, R.: More effective choice in the prisoner’s dilemma. Journal of Conflict Resolution 24(3), 379–403 (1980)

    Article  Google Scholar 

  4. Axelrod, R.: The Evolution of Cooperation. Basic Books, New York (1984)

    Google Scholar 

  5. Axelrod, R.: The evolution of strategies in the iterated prisoners’ dilemma. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, pp. 32–42. Morgan Kaufmann, Los Altos (1987)

    Google Scholar 

  6. Axelrod, R., Hamilton, W.D.: The evolution of cooperation. Science 211, 1390–1396 (1981)

    Article  MathSciNet  Google Scholar 

  7. Binmore, K.: Fun and Games. D.C.Heath, Lexington (1991)

    Google Scholar 

  8. Binmore, K.: Playing fair: Game theory and the social contract. MIT Press, Cambridge (1994)

    Google Scholar 

  9. Bratman, M.E.: Shared cooperative activity. The Philosophical Review 101(2), 327–341 (1992)

    Article  Google Scholar 

  10. Byrd, R.H., Dert, C.L., Rinnooy Kan, A.H.G., Schnabel, R.B.: Concurrent stochastic methods for global optimization. Mathematical Programming 46, 1–30 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  11. Colman, A.M.: Game Theory and Experimental Games. Pergamon Press Ltd., Oxford (1982)

    Google Scholar 

  12. Doran, J.E., Franklin, S., Jennings, N.R., Norman, T.J.: On cooperation in multi-agent systems. The Knowledge Engineering Review 12(3), 309–314 (1997)

    Article  Google Scholar 

  13. Nash, J.F.: Non-cooperative games. Annals of Mathematics 54(2), 286–295 (1951)

    Article  MathSciNet  Google Scholar 

  14. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  15. Linster, B.: Essays on Cooperation and Competition. PhD thesis, University of Michigan, Michigan (1990)

    Google Scholar 

  16. Luce, R., Raiffa, H.: Games and Decisions. Wiley, New York (1957)

    MATH  Google Scholar 

  17. Murty, K.G., Kabadi, S.N.: Some NP-complete problems in quadratic and nonlinear programming. Mathematical Programming 39, 117–130 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  18. Töreyen, Ö.: A game-theory based multi-agent system for solving complex optimisation problems and a clustering application related to the integration of turkey into the eu community. M.Sc. Thesis Submitted to the Department of Mathematical Sciences, University of Essex, UK (2008)

    Google Scholar 

  19. Park, S., Sugumaran, V.: Designing multi-agent systems: A framework and application. Expert Systems with Applications 28, 259–271 (2005)

    Article  Google Scholar 

  20. Rapoport, A., Chammah, A.M.: Prisoner’s Dilemma: A Study in Conflict and Cooperation. University of Michigan Press, Ann Arbor (1965)

    Google Scholar 

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

  22. Rinnooy Kan, A.H.G., Timmer, G.T.: Stochastic global optimization methods Part I: Clustering methods. Mathematical Programming 39, 27–56 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  23. Rinnooy Kan, A.H.G., Timmer, G.T.: Stochastic global optimization methods Part II: Multi-level methods. Mathematical Programming 39, 57–78 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  24. Rinnooy Kan, A.H.G., Timmer, G.T.: Global optimization. In: Nemhauser, G.L., Rinnooy Kan, A.H.G., Todd, M.J. (eds.) Optimization. Handbooks in Operations Research and Management Science, ch. IX, vol. 1, pp. 631–662. North Holland, Amsterdam (1989)

    Chapter  Google Scholar 

  25. Salhi, A., Glaser, H., De Roure, D.: A genetic approach to understanding cooperative behaviour. In: Osmera, P. (ed.) Proceedings of the 2nd International Mendel Conference on Genetic Algorithms, MENDEL 1996, pp. 129–136 (1996)

    Google Scholar 

  26. Salhi, A., Glaser, H., De Roure, D.: Parallel implementation of a genetic-programming based tool for symbolic regression. Information Processing Letters 66(6), 299–307 (1998)

    Article  Google Scholar 

  27. Salhi, A., Glaser, H., De Roure, D., Putney, J.: The prisoners’ dilemma revisited. Technical Report DSSE-TR-96-2, Department of Electronics and Computer Science, The University of Southampton, U.K. (February 1996)

    Google Scholar 

  28. Salhi, A., Proll, L.G., Rios Insua, D., Martin, J.: Experiences with stochastic algorithms for a class of global optimisation problems. RAIRO Operations Research 34(22), 183–197 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  29. Seshadri, A.: Simulated annealing for travelling salesman problem, http://www.mathworks.com/matlabcentral/fileexchange

  30. Tweedale, J., Ichalkaranje, H., Sioutis, C., Jarvis, B., Consoli, A., Phillips-Wren, G.: Innovations in multi-agent systems. Journal of Network and Computer Applications 30, 1089–1115 (2007)

    Article  Google Scholar 

  31. Wooldridge, M., Jennings, N.R.: Intelligent agents: Theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)

    Article  Google Scholar 

  32. Zhigljavsky, A.A.: Theory of Global Search. Mathematics and its applications, Soviet Series, vol. 65. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, The University of Essex, Colchester, CO4 3SQ, UK

    Abdellah Salhi & Özgun Töreyen

Authors
  1. Abdellah Salhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Özgun Töreyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering and Science-Faculty of Engineering, Kyoto University, Yoshida-honmachi,Sakyo-Ku, 606-8501, Kyoto, Japan

    Yoel Tenne

  2. Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Block N2,Level B3C,Unit 05-08, 639798, Singapore

    Chi-Keong Goh

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Salhi, A., Töreyen, Ö. (2010). A Game Theory-Based Multi-Agent System for Expensive Optimisation Problems. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Optimization. Adaptation, Learning, and Optimization, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12775-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12775-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12774-8

  • Online ISBN: 978-3-642-12775-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation