Skip to main content

A Fast and Effective Method for Pruning of Non-dominated Solutions in Many-Objective Problems

  • Conference paper
Parallel Problem Solving from Nature - PPSN IX (PPSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4193))

Included in the following conference series:

Abstract

Diversity maintenance of solutions is an essential part in multi-objective optimization. Existing techniques are suboptimal either in the sense of obtained distribution or execution time. This paper proposes an effective and relatively fast method for pruning a set of non-dominated solutions. The proposed method is based on a crowding estimation technique using nearest neighbors of solutions in Euclidean sense, and a technique for finding these nearest neighbors quickly. The method is experimentally evaluated, and results indicate a good trade-off between the obtained distribution and execution time. Distribution is good also in many-objective problems, when number of objectives is more than two.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of the Third Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2001), Athens, Greece, pp. 95–100 (2002)

    Google Scholar 

  2. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  3. Jensen, M.T.: Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Transactions on Evolutionary Computation 7(5), 503–515 (2003)

    Article  Google Scholar 

  4. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Evolutionary Multiobjective Optimization, pp. 105–145. Springer, London (2005)

    Chapter  Google Scholar 

  5. Kukkonen, S., Deb, K.: Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems. In: Proceedings of the 2006 Congress on Evolutionary Computation (CEC 2006), Vancouver, BC, Canada (2006) (accepted for publication)

    Google Scholar 

  6. Guan, L., Kamel, M.: Equal-average hyperplane partioning method for vector quantization of image data. Pattern Recognition Letters 13(10), 693–699 (1992)

    Article  Google Scholar 

  7. Ra, S.W., Kim, J.K.: A fast mean-distance-ordered partial codebook search algorithm for image vector quantization. IEEE Transactions on Circuits and Systems-II 40(9), 576–579 (1993)

    Article  Google Scholar 

  8. Baek, S., Sung, K.M.: Fast K-nearest-neighbour search algorithm for nonparametric classification. IEE Electronics Letters 36(21), 1821–1822 (2000)

    Article  Google Scholar 

  9. Bei, C.D., Gray, R.M.: An improvement of the minimum distortion encoding algorithm for vector quantization. IEEE Transactions on Communications COM-33(10), 1132–1132 (1985)

    Google Scholar 

  10. Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. Prentice-Hall, Englewood Cliffs (1990)

    MATH  Google Scholar 

  11. Vaidya, P.M.: An O(n logn) algorithm for the all-nearest-neigbors problem. Discrete & Computational Geometry 4, 101–115 (1989)

    Article  MathSciNet  Google Scholar 

  12. Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of Generalized Differential Evolution. In: Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005), Edinburgh, Scotland, pp. 443–450 (2005)

    Google Scholar 

  13. Price, K.V., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  14. Inorio, A., Li, X.: Solving rotated multi-objective optimization problems using Differential Evolution. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 861–872. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, England (2001)

    MATH  Google Scholar 

  16. Kukkonen, S.: A fast and effective method for pruning of non-dominated solutions in many-objective problems, results (2006) (June 15, 2006), Available: http://www.it.lut.fi/ip/evo/pruning

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kukkonen, S., Deb, K. (2006). A Fast and Effective Method for Pruning of Non-dominated Solutions in Many-Objective Problems. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_56

Download citation

  • DOI: https://doi.org/10.1007/11844297_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38990-3

  • Online ISBN: 978-3-540-38991-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics