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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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.
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.
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.
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.
Leandro N. de Castro and Jonathan Timmis. Artificial Immune Systems:ANewComputational Intelligence Approach. Springer, London, 2002.
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.
Kalyanmoy Deb. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, UK, 2001. ISBN 0-471-87339-X.
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.
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.
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.
Joshua D. Knowles and DavidW. Corne. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation, 8(2):149–172, 2000.
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.
Kaisa M. Miettinen. Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston, Massachusetts, 1998.
J. David Schaffer. Multiple Objective Optimization withVector Evaluated Genetic Algorithms. PhD thesis, Vanderbilt University, 1984.
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.
N. Srinivas and Kalyanmoy Deb. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3):221–248, Fall 1994.
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.
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.
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.
J. Yoo and P. Hajela. Immune network simulations in multicriterion design. Structural Optimization, 18:85–94, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cortés, N.C., Coello, C.A.C. (2003). Multiobjective Optimization Using Ideas from the Clonal Selection Principle. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_22
Download citation
DOI: https://doi.org/10.1007/3-540-45105-6_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40602-0
Online ISBN: 978-3-540-45105-1
eBook Packages: Springer Book Archive