Skip to main content
SpringerLink
Log in
Book cover

International Conference on Neural Information Processing

ICONIP 2015: Neural Information Processing pp 344–355Cite as

A Feature-Based Analysis on the Impact of Set of Constraints for \(\varepsilon \)-Constrained Differential Evolution

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

Abstract

Different types of evolutionary algorithms have been developed for constrained continuous optimisation. We carry out a feature-based analysis of evolved constrained continuous optimisation instances to understand the characteristics of constraints that make problems hard for evolutionary algorithm. In our study, we examine how various sets of constraints can influence the behaviour of \(\varepsilon \)-Constrained Differential Evolution. Investigating the evolved instances, we obtain knowledge of what type of constraints and their features make a problem difficult for the examined algorithm.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. Floudas, C.A., Pardalos, P.M.: A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1990)

    MATH  Google Scholar 

  2. Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking 2010: Experimental setup (2010)

    Google Scholar 

  3. Mallipeddi, R., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Nanyang Technological University, Singapore (2010)

    Google Scholar 

  4. Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., Rudolph, G.: Exploratory landscape analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 829–836. ACM (2011)

    Google Scholar 

  5. Mersmann, O., Preuss, M., Trautmann, H.: Benchmarking evolutionary algorithms: towards exploratory landscape analysis. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 73–82. Springer, Heidelberg (2010)

    Google Scholar 

  6. Mezura-Montes, E., Coello Coello, C.A.: Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol. Comput. 1(4), 173–194 (2011)

    Article  Google Scholar 

  7. Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., Trautmann, H.: A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. In: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII, pp. 147–160. ACM (2013)

    Google Scholar 

  8. Poursoltan, S., Neumann, F.: A feature-based analysis on the impact of linear constraints for \(\varepsilon \)-constrained differential evolution. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3088–3095. IEEE (2014)

    Google Scholar 

  9. Poursoltan, S., Neumann, F.: Ruggedness quantifying for constrained continuous fitness landscapes. In: Datta, R., Deb, K. (eds.) Evolutionary Constrained Optimization, pp. 29–50. Springer, Heidelberg (2015)

    Google Scholar 

  10. Smith-Miles, K., van Hemert, J., Lim, X.Y.: Understanding TSP difficulty by learning from evolved instances. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 266–280. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  11. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Takahama, T., Sakai, S.: Constrained optimization by the \(\varepsilon \) constrained differential evolution with an archive and gradient-based mutation. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–9. IEEE (2010)

    Google Scholar 

Download references

Frank Neumann has been supported by ARC grants DP130104395 and DP140103400.

Author information

Authors and Affiliations

  1. Optimisation and Logistics, School of Computer Science, University of Adelaide, Adelaide, SA, 5005, Australia

    Shayan Poursoltan & Frank Neumann

Authors
  1. Shayan Poursoltan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shayan Poursoltan or Frank Neumann .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Poursoltan, S., Neumann, F. (2015). A Feature-Based Analysis on the Impact of Set of Constraints for \(\varepsilon \)-Constrained Differential Evolution. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26555-1_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation