Skip to main content
SpringerLink
Log in

Set-Based Many-Objective Optimization Guided by Preferred Regions

  • Conference paper
  • First Online:
Advanced Intelligent Computing Theories and Applications (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

Included in the following conference series:

  • 2987 Accesses

Abstract

Set-based evolutionary optimization based on the performance indicators is one of the effective methods to solve many-objective optimization problems; however, previous researches didn’t make full use of the preference information of a high-dimensional objective space to guide the evolution of a population. In this study, we propose a set-based many-objective evolutionary optimization algorithm guided by preferred regions. In the mode of set-based evolution, the proposed method dynamically determines a preferred region of the high-dimensional objective space, designs a selection strategy on sets by combining Pareto dominance relation on sets with the above preferred region, and develops a crossover operator on sets guided by the above preferred region to produce a Pareto front with superior performance. The proposed method is applied to four benchmark many-objective optimization problems, and the experimental results empirically demonstrate its effectiveness.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Deb, K., Jain, H.: An Improved NSGA-II Procedure for Many-objective Optimization, Part I: Solving Problems with Box Constraints. KanGAL Report (2012)

    Google Scholar 

  2. Deb, K., Prata, P.A., Agarwal, S.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2(6), 182–197 (2002)

    Article  Google Scholar 

  3. Reyes-Sierra, M., Coello, C.C.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  4. Sato, H., Aguirre, H.E., Tanaka, K.: Controlling dominance area of solutions and its impact on the performance of MOEAs. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 5–20. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Deb, K., Saxena, D.K.: On Finding Pareto-optimal Solutions through Dimensionality Reduction for Certain Large-dimensional Multi-objective Optimization Problems. KanGAL Report (2005)

    Google Scholar 

  6. Deb, K., Saxena, D.: Searching for pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: Proceedings of the World Congress on Computational Intelligence (2006)

    Google Scholar 

  7. Zhang, Q.F., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  8. Gong, D., Ji, X.: Solving many-objective optimization problems using set-based evolutionary algorithm. Chin. J. Electron. 42(1), 77–83 (2014)

    Google Scholar 

  9. Zitzler, E., Thiele, L., Bader, J.: On set-based multi-objective optimization. IEEE Trans. Evol. Comput. 14(1), 58–79 (2010)

    Article  MATH  Google Scholar 

  10. Bader, J., Brockhoff, D., Welten, S., Zitzler, E.: On using populations of sets in multiobjective optimization. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 140–154. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  11. Gong, D., Ji, X., Sun, J.: Interactive evolutionary algorithms with decision-maker’s preferences for solving interval multi-objective optimization problems. Neurocomputing 137, 241–251 (2014)

    Article  Google Scholar 

  12. Yang, D., Jiao, L., Gong, M., Yu, H.: Clone selection algorithm to solve preference multi-objective optimization. J. Softw. 21(1), 14–33 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  13. Liu, R., Wang, X., Liu, J.: A preference multi-objective optimization based on adaptive rank clone and differential evolution. Nat. Comput. 12(1), 109–132 (2013)

    Article  MathSciNet  Google Scholar 

  14. Ehrgott, M.: Multicriteria optimization. Springer Science and Business Media (2006)

    Google Scholar 

  15. López-Jaimes, A., CoelloCoello, C.A.: Including preferences into a multi-objective evolutionary algorithm to deal with many-objective engineering optimization problems. Inf. Sci. 277, 1–20 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

This work was jointly supported by National Natural Science Foundation of China with grant No. 61375067 and 61403155, National Basic Research Program of China (973 Program) with grant No. 2014CB046306-2, and Natural Science Foundation of Jiangsu Province with grant No. BK2012566

Author information

Authors and Affiliations

  1. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Dunwei Gong, Fenglin Sun & Xiaoyan Sun

  2. College of Science, Huai Hai Institute of Technology, Lianyungang, 222005, Jiangsu, China

    Jing Sun

Authors
  1. Dunwei Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fenglin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoyan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Fenglin Sun , Jing Sun or Xiaoyan Sun .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gong, D., Sun, F., Sun, J., Sun, X. (2015). Set-Based Many-Objective Optimization Guided by Preferred Regions. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22053-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation