Skip to main content

Multiobjective Clonal Selection Algorithm for the Forecasting Models on the Base of the Strictly Binary Trees

  • Conference paper
  • First Online:
  • 1537 Accesses

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

Abstract

In this paper, a multiobjective modified clonal selection algorithm based on the use of the notion «Pareto set», which can be applied for the development of the forecasting models on the base of the strictly binary trees has been offered. It is suggested to use the affinity indicator based on the average forecasting error rate, and the tendencies discrepancy indicator in the role of the objective functions. The results of experimental studies which confirm the efficiency of the offered multiobjective clonal selection algorithm have been given.

This work is supported by Russian Federal Property Fund, 16-08-00771.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  1. Demidova, L.A.: Time series forecasting models on the base of modified clonal selection algorithm. In: 2014 International Conference on Computer Technologies in Physical and Engineering Applications, pp. 33–34 (2014)

    Google Scholar 

  2. Demidova, L.A.: Assessment of the quality prediction models based of the strict on binary trees and the modified clonal selection algorithm. Cloud Sci. 1, 202–222 (2014). (in Russian)

    Google Scholar 

  3. Demidova, L.A.: Genetic algorithm for optimal parameters search in the one-factor forecasting model based on continuous type-2 fuzzy sets. Autom. Remote Control 74(2), 313–320 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Astakhova, N.N., Demidova, L.A., Nikulchev, E.V.: Forecasting of time series’ groups with application of fuzzy C-mean algorithm. Contemp. Eng. Sci. 8(35), 1659–1677 (2015)

    Article  Google Scholar 

  5. Astakhova, N., Demidova, L., Nikulchev, E., Pluzhnik, E.: Forecasting of time series’ groups with application of fuzzy C-mean algorithm and forecasting models on the base of strictly binary trees and modified clonal selection algorithm. In: 16th International Symposium on Advanced Intelligent Systems, pp. 861–873 (2015)

    Google Scholar 

  6. Astakhova, N.N., Demidova, L.A., Nikulchev, E.V.: Forecasting method for grouped time series with the use of K-means algorithm. Appl. Math. Sci. 9(97), 4813–4830 (2015)

    Google Scholar 

  7. Astakhova, N., Demidova, L., Konev, V.: The description problem of the clusters’ centroids. In: 2015 International Conference “Stability and Control Processes” in Memory of V.I. Zubov (SCP), pp. 448–451 (2015)

    Google Scholar 

  8. Astakhova, N., Demidova, L.: Using of the notion «Pareto set» for development of the forecasting models based on the modified clonal selection algorithm. In: 6th Seminar on Industrial Control Systems: Analysis, Modeling and Computation, Art. 02001 (2016)

    Google Scholar 

  9. Michalewicz, Z.: Genetic algorithms, numerical optimization and constraints. In: Proceedings of the Sixth International Conference on Genetic Algorithms and their Applications, Pittsburgh, PA, pp. 239–247 (1995)

    Google Scholar 

  10. Goldberg, D.E.: Genetic Algorithms in Search Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  11. Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms – part i: a unified formulation. Technical report 564, University of Sheffield, Sheffield, UK, pp. 1–16 (1995)

    Google Scholar 

  12. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, Piscataway, vol. 1, pp. 82–87 (1994)

    Google Scholar 

  13. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms – a comparative case study. In: Eiben, V.A.E., Back, T., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel Problem Solving From Nature, pp. 292–301. Springer, Berlin (1998)

    Google Scholar 

  14. Knowles, J., Corne, D.: The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 98–105. IEEE Service Center, Piscataway (1999)

    Google Scholar 

  15. Rudolph, G.: Evolutionary search under partially ordered sets. Department of Computer Science/LS11, University of Dortmund, Dortmund, Germany, Technical report CI-67/99 (1999)

    Google Scholar 

  16. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA II. KanGAL Report No. 200001, pp. 182–197. Indian Institute of Technology, Kanpur (2000)

    Google Scholar 

  17. Deb, K.: Multi-objective Optimization using Evolutionary Algorithms, pp. 221–232. Wiley, Chichester (2001)

    MATH  Google Scholar 

  18. Seada, H., Deb, K.: U-NSGA-III: a unified evolutionary optimization procedure for single, multiple, and many objectives: proof-of-principle results. In: Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science, vol. 9019, pp. 34–49 (2015)

    Google Scholar 

  19. Coello, P., Coello, C.A., Cruz Cortés, N.: An approach to solve multiobjective optimization problems based on an artificial immune system. In: Proceedings of the First International Conference on Artificial Immune Systems, University of Kent, Canterbury, UK, 9–11 September 2002, pp. 212–221 (2002)

    Google Scholar 

  20. Luh, G.-C., Chueh, C.-H., Liu, W.-W.: MOIA: multi-objective immune algorithm. Comput. Struct. 82, 829–844 (2004)

    Article  Google Scholar 

  21. Campelo, F., Guimarães, F.G., Saldanha, R.R., Igarashi, H., Noguchi, S., Lowther, D.A., Ramirez, J.A.: A novel multiobjective immune algorithm using nondominated sorting. In: 11th International IGTE Symposium on Numerical Field Calculation in Electrical Engineering (2004)

    Google Scholar 

  22. Jiao, L., Gong, M., Shang, R., Du, H., Lu, B.: Clonal selection with immune dominance and anergy based multiobjective optimization. In: 3rd International Conference on Evolutionary Multi-criterion Optimization, pp. 474–489 (2005)

    Google Scholar 

  23. Wang, X.L., Mahfouf, M.: ACSAMO: an adaptive multiobjective optimization algorithm using the clonal selection principle. In: 2nd European Symposium on Nature-Inspired Smart Information Systems, pp. 1–12 (2006)

    Google Scholar 

  24. Zhang, Z.: Constrained multiobjective optimization immune algorithm: convergence and application. Comput. Math. Appl. 52(5), 791–808 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  25. Jiao, L., Gong, M., Du, H., Bo, L.: Multiobjective immune algorithm with nondominated neighbor-based selection. Evolut. Comput. 16(2), 225–255 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Evgeny Nikulchev .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Astakhova, N., Demidova, L., Nikulchev, E. (2018). Multiobjective Clonal Selection Algorithm for the Forecasting Models on the Base of the Strictly Binary Trees. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56994-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics