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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2001: Evolutionary Multi-Criterion Optimization pp 141–153Cite as

Evolutionary Algorithms for Multicriteria Optimization with Selecting a Representative Subset of Pareto Optimal Solutions

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Abstract

In this paper the method of selecting a representative subset of Pareto optimal solutions is used to make the search of Pareto frontier more effective. Firstly, the evolutionary algorithm method for generating a set of Pareto optimal solutions is described. Then, indiscernibility interval method is applied to select representative subset of Pareto optimal solutions. The main idea of this method consists in removing from the set of Pareto optimal solutions these solutions, which are close to each other in the space of objectives, i.e., those solutions for which the values of the objective functions differ less than an indiscernibility interval. The set of Pareto optimal solutions is reduced using indiscernibility interval method after running a certain number of generations. This process can be called the filtration process in which less important Pareto optimal solutions are removed from the existing set. Finally, two design optimization problems are solved using the proposed method. From these examples it is clear that the computation time can be reduced significantly and still the real Pareto frontier obtained.

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References

  1. Coello C. A. C. (1999) A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques, Knowledge and Information Systems, Vol. 1, No. 3, pp. 269–308.

    Google Scholar 

  2. Krenich, S. & Osyczka, A. (2000): Optimization of Robot Grippers Parameters Using Genetic Algorithms. The 13th CISM-IFToMM Symposium on the Theory and Practice of Robots and Manipulators. July 3-6, 2000, Zakopane, Poland.

    Google Scholar 

  3. Montusiewicz J. (1999) Division of the Set of Nondominated Solutions by Means of the Undifferentiation Interval Method, In: The Technological Information Systems, pp. 65–72, Societas Scientiarum Lublinensis, Lublin.

    Google Scholar 

  4. Osyczka A., Montusiewicz J. (1994) A Random Search Approach to Multicriterion Discrete Optimization, IUTAM Symposium on Discrete Structural Optimization, Springer-Verlag, pp. 71–79.

    Google Scholar 

  5. Osyczka A., Tamura H., Saito Y. (1997) Pareto Set Distribution Method for Multicriteria Design Optimization Using Genetic Algorithm, In: Proceedings of Engineering Design and Automation Conference, Bangkok, Thailand, pp. 228–231.

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  6. Osyczka A., Tamura H. (1996) Pareto Set Distribution Method for Multicriteria Optimization Using Genetic Algorithm, In: Proceedings of Genetic Algorithms’ 96, (MENDEL-96)-International Conference, Brno, Czech Republic, pp.135–143.

    Google Scholar 

  7. Steuer R.E. (1986) Multiple Criteria Optimization-Theory, Computation and Application, Wiley, New York.

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Cracow University of Technology, Al.Jana Pawla II 37, 31-864, Krakow, Poland

    Andrzej Osyczka & Stanislaw Krenich

Authors
  1. Andrzej Osyczka
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  2. Stanislaw Krenich
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Osyczka, A., Krenich, S. (2001). Evolutionary Algorithms for Multicriteria Optimization with Selecting a Representative Subset of Pareto Optimal Solutions. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_10

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  • DOI: https://doi.org/10.1007/3-540-44719-9_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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