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Multiobjective Optimization Using Ideas from the Clonal Selection Principle

  • Nareli Cruz Cortés
  • Carlos A. Coello Coello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

In this paper, we propose a new multiobjective optimization approach based on the clonal selection principle. Our approach is compared with respect to other evolutionary multiobjective optimization techniques that are representative of the state-of-the-art in the area. In our study, several test functions and metrics commonly adopted in evolutionary multiobjective optimization are used. Our results indicate that the use of an artificial immune system for multiobjective optimization is a viable alternative.

Keywords

Pareto Front Multiobjective Optimization External Memory Multiobjective Optimization Problem Nondominated Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kevin P. Anchor, Jesse B. Zydallis, Gregg H. Gunsch, and Gary B. Lamont. Extending the Computer Defense Immune System: Network Intrusion Detection with a Multiobjective Evolutionary Programming Approach. In Jonathan Timmis and Peter J. Bentley, editors, First International Conference on Artificial Immune Systems (ICARIS’2002), pages 12–21. University of Kent at Canterbury, UK, September 2002. ISBN 1-902671-32-5.Google Scholar
  2. 2.
    Carlos A. Coello Coello and Nareli Cruz Cortés. An Approach to Solve Multiobjective Optimization Problems Based on an Artificial Immune System. In Jonathan Timmis and Peter J. Bentley, editors, First International Conference on Artificial Immune Systems (ICARIS’2002), pages 212–221. University of Kent at Canterbury, UK, September 2002. ISBN 1-902671-32-5.Google Scholar
  3. 3.
    Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York, May 2002. ISBN 0-3064-6762-3.zbMATHGoogle Scholar
  4. 4.
    Indraneel Das and John Dennis. A Closer Look at Drawbacks of Minimizing Weighted Sums of Objectives for Pareto Set Generation in Multicriteria Optimization Problems. Structural Optimization, 14(1):63–69, 1997.CrossRefGoogle Scholar
  5. 5.
    Leandro N. de Castro and Jonathan Timmis. Artificial Immune Systems:ANewComputational Intelligence Approach. Springer, London, 2002.Google Scholar
  6. 6.
    Leandro Nunes de Castro and F. J. Von Zuben. Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, 6(3):239–251, 2002.CrossRefGoogle Scholar
  7. 7.
    Kalyanmoy Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, UK, 2001. ISBN 0-471-87339-X.zbMATHGoogle Scholar
  8. 8.
    Kalyanmoy Deb, Samir Agrawal, Amrit Pratab, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-bjective Optimization: NSGA-II. In Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel, editors, Proceedings of the Parallel Problem Solving from Nature VI Conference, pages 849–858, Paris, France, 2000. Springer. Lecture Notes in Computer Science No. 1917.CrossRefGoogle Scholar
  9. 9.
    Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, April 2002.CrossRefGoogle Scholar
  10. 10.
    Hajime Kita, Yasuyuki Yabumoto, Naoki Mori, and Yoshikazu Nishikawa. Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm. In Hans-Michael Voigt, Werner Ebeling, Ingo Rechenberg, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature-PPSN IV, Lecture Notes in Computer Science, pages 504–512, Berlin, Germany, September 1996. Springer-Verlag.CrossRefGoogle Scholar
  11. 11.
    Joshua D. Knowles and DavidW. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.CrossRefGoogle Scholar
  12. 12.
    Frank Kursawe. A Variant of Evolution Strategies for Vector Optimization. In H. P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature. 1st Workshop, PPSN I, volume 496 of Lecture Notes in Computer Science, pages 193–197, Berlin, Germany, oct 1991. Springer-Verlag.CrossRefGoogle Scholar
  13. 13.
    Kaisa M. Miettinen. Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston, Massachusetts, 1998.Google Scholar
  14. 14.
    J. David Schaffer. Multiple Objective Optimization withVector Evaluated Genetic Algorithms. PhD thesis, Vanderbilt University, 1984.Google Scholar
  15. 15.
    Jason R. Schott. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, May 1995.Google Scholar
  16. 16.
    N. Srinivas and Kalyanmoy Deb. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3):221–248, Fall 1994.CrossRefGoogle Scholar
  17. 17.
    David A. Van Veldhuizen. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999.Google Scholar
  18. 18.
    David A. Van Veldhuizen and Gary B. Lamont. MOEA Test Suite Generation, Design & Use. In Annie S. Wu, editor, Proceedings of the 1999 Genetic and Evolutionary Computation Conference. Workshop Program, pages 113–114, Orlando, Florida, July 1999.Google Scholar
  19. 19.
    David A. Van Veldhuizen and Gary B. Lamont. On Measuring Multiobjective Evolutionary Algorithm Performance. In 2000 Congress on Evolutionary Computation, volume 1, pages 204–211, Piscataway, New Jersey, July 2000. IEEE Service Center.CrossRefGoogle Scholar
  20. 20.
    J. Yoo and P. Hajela. Immune network simulations in multicriterion design. Structural Optimization, 18:85–94, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nareli Cruz Cortés
    • 1
  • Carlos A. Coello Coello
    • 1
  1. 1.Evolutionary Computation Group, Depto. de Ingeniería Eléctrica, Sección de ComputaciónCINVESTAV-IPNMéxico, D. F.Mexico

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