Immunity-Based Multi-Agent Coalition Formation for Elimination of Oil Spills

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)

Abstract

Occurrence of oil spills is a serious ecological problem which negatively influences the environment, especially water ecosystems. It is necessary to use efficient approaches that can reduce this danger as fast as possible. Multi-agent coalition formation is investigated in conjunction with the immunity-based algorithm CLONALG-Opt for elimination of oil spills.

Keywords

Coalition agent immunity lymphatic system CLONALG-Opt 

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References

  1. 1.
    MIT: MIT researchers unveil autonomous oil-absorbing robot. In: MIT Media Relations (2010), http://web.mit.edu/press/2010/seaswarm.html
  2. 2.
    Fritsch, D., Wegener, K., Schraft, R.D.: Sensor concept for robotic swarms for the elimination of marine oil pollutions. In: Proceedings of the Joint Conference on Robotics. ISR 2006, The 37th International Symposium on Robotics, vol. 156(1-3), pp. 880–887 (2006)Google Scholar
  3. 3.
    Turan, O., et al.: Design and Operation of Small to Medium scale Oil-spill Cleaning Units. In: Proceedings of the International Conference on Towing and Salvage of Disabled Tankers Safetow, United Kingdom (2007)Google Scholar
  4. 4.
    Gan, Z., Li, G., Yang, Z., Jiang, M.: Automatic Modeling of Complex Functions with Clonal Selection-based Gene Expression Programming. In: The Third International Conference on Natural Computation (ICNC 2007), vol. 4, pp. 228–232 (2007)Google Scholar
  5. 5.
    Dias, M.B., et al.: Market-Based Multirobot Coordination: A Survey and Analysis. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania (2005)Google Scholar
  6. 6.
    Sandholm, T., et al.: Anytime Coalition Structure Generation with Worst Case Guarantees. In: AAAI 1998/IAAI 1998 Proceedings of the Fifteenth National Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, pp. 46–53. American Association for Artificial Intelligence, Menlo Park (1998)Google Scholar
  7. 7.
    Sandholm, T., et al.: Coalition Structure Generation with Worst Case Guarantees. In: Artificial Intelligence, vol. 111(1-2), pp. 209–238 (1999)Google Scholar
  8. 8.
    Rahwan, T.: Algorithms for Coalition Formation in Multi-Agent Systems. University of Southhampton. Dissertation Thesis, p. 132 (2007)Google Scholar
  9. 9.
    Sen, S., Dutta, S.: Searching for optimal coalition structures. In: Proceedings of Fourth International Conference on Multi Agent Systems, pp. 287–292 (2000)Google Scholar
  10. 10.
    Ahmadi, M., Sayyadian, M., Rabiee, H.R.: Coalition Formation for Task Allocation via Genetic Algorithms. In: Advances in Information and Communication Technology. Springer, Germany (2002)Google Scholar
  11. 11.
    De Castro, L., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach, p. 398. Springer (2002)Google Scholar
  12. 12.
    Castro, L.N., Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transaction on Evolutionary Computation 6(3), 239–251 (2002)CrossRefGoogle Scholar
  13. 13.
    Castro, L.N., Zuben, F.J.: The Clonal Selection Algorithm With Engineering Application. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 36–37. Morgan Kaufmann, San Franciso (2002)Google Scholar
  14. 14.
    Castro, L.N.: Fundamentals of natural computing: basic concepts, algorithms, and applications, 1st edn., p. 696. Chapman and Hall/CRC (2006)Google Scholar
  15. 15.
    Krynyckyi, B.R., et al.: Clinical Breast Lymphoscintigraphy: Optimal Techniques for Performing Studies, Image Atlas, and Analysis of Images. Radiographics 24, 121–145 (2004), http://radiographics.rsna.org/content/24/1/121.full CrossRefGoogle Scholar
  16. 16.
    Husáková, M.: Multi-Agent Coalition Formation – Algorithm Clonalg-Opt. (2012), http://edu.uhk.cz/~fshusam2/ClonalgOpt-final.html
  17. 17.
    De Castro, L.N., Timmis, J.I.: Artificial immune systems as a novel soft computing paradigm. In: Soft Computing, vol. 7, pp. 526–544. Springer (2003)Google Scholar
  18. 18.
    Hajela, P., Yoo, J., Lee, J.: GA based simulation of immune networks – applications in structural optimization. Journal of Engineering Optimization (29), 131–149 (1997)CrossRefGoogle Scholar
  19. 19.
    Nasaroui, O., Gonzalez, F., Dasgupta, D.: The Fuzzy Artificial Immune System: Motivations, Basic Concepts, and Application to Clustering and Web Profiling. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2002), vol. 1, pp. 711–716 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Information Technologies, Department of Information TechnologiesUniversity of Hradec KrálovéHradec KrálovéCzech Republic

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