Skip to main content
SpringerLink
Log in

Solving Dynamic Optimisation Problems with Known Changeable Boundaries

  • Conference paper
  • First Online:
Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

  • 1204 Accesses

Abstract

Dynamic optimisation problems (DOPs) have become a challenging research topic over the last two decades. In DOPs, at least one part of the problem changes as time passes. These changes may take place in the objective function(s) and/or constraint(s). In this paper, we propose a new type of DOP in which the boundaries of variables change as time passes. This is called a single objective unconstrained dynamic optimisation problem with known changeable boundaries (DOPKCBs). To solve DOPKCBs, we propose three repair strategies. These algorithms have been compared with other repairing techniques from the literature that have been previously used in static problems. In this paper, the results of the conducted experiments and the statistical analysis generally demonstrated that one of the proposed strategies, which uses the overall elite individual (OEI) as a repair strategy, obtained much better results than the other strategies.

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. Gendreau, M., Potvin, J.-Y., Bräysy, O., Hasle, G., Løkketangen, A.: Metaheuristics for the vehicle routing problem and extensions: a categorized bibliography. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Springer, New York (2008)

    Google Scholar 

  2. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006)

    MATH  Google Scholar 

  3. Miettinen, K., Ruiz, F., Wierzbicki, A.P.: Introduction to multiobjective optimization: interactive approaches. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 27–57. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  4. Bandyopadhyay, S., Saha, S.: some single - and multiobjective optimization techniques. In: Unsupervised Classification, pp. 17–58. Springer, Heidelberg (2013)

    Google Scholar 

  5. Dadkhah, K.: Static optimization. In: Foundations of Mathematical and Computational Economics, pp. 323–346. Springer, Berlin (2011)

    Google Scholar 

  6. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)

    Google Scholar 

  7. Nguyen, T.T., Yangb, S., Branke, J.: Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol. Comput. 6, 1–24 (2012)

    Article  Google Scholar 

  8. Cruz, C., González, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft. Comput. 15, 1427–1448 (2011)

    Article  Google Scholar 

  9. Wen-Jun, Z., Xiao-Feng, X., De-Chun, B.: Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: Congress on Evolutionary Computation, CEC 2004, vol. 2302, pp. 2307–2311 (2004)

    Google Scholar 

  10. Shi, C., Yuhui, S., Quande, Q.: Experimental study on boundary constraint handling in particle swarm optimization: from population diversity perspective. In: Yuhui, S. (ed.) Recent Algorithms and Applications in Swarm Intelligence Research, pp. 96–124. IGI Global, Hershey (2013)

    Chapter  Google Scholar 

  11. Padhye, N., Deb, K., Mittal, P.: An Efficient and Exclusively-Feasible Constrained Handling Strategy for Evolutionary Algorithms. Technical Report (2013)

    Google Scholar 

  12. Yang, S.: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol. Comput. 16, 385–416 (2008)

    Article  Google Scholar 

  13. Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Advances in Evolutionary Computing: Theory and Applications, pp. 239–262. Springer, Heidelberg (2003)

    Google Scholar 

  14. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Naval Research Laboratory (1990)

    Google Scholar 

  15. Goh, C.-K., Chen Tan, K.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 13, 103–127 (2009)

    Article  Google Scholar 

  16. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, vol. 2, pp. 137–144. North Holland, Amsterdam (1992)

    Google Scholar 

  17. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft. Comput. 9, 815–834 (2005)

    Article  MATH  Google Scholar 

  18. Nguyen, T.T., Yang, S., Branke, J., Yao, X.: Evolutionary dynamic optimization: methodologies. In: Yang, S., Yao, X. (eds.) Evolutionary Computation for DOPs. SCI, vol. 490, pp. 39–63. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 1883, p. 1882 (1999)

    Google Scholar 

  20. Moser, I., Chiong, R.: Dynamic function optimization: the moving peaks benchmark. In: Alba, E., Nakib, A., Siarry, P. (eds.) Metaheuristics for Dynamic Optimization. SCI, vol. 433, pp. 37–62. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  21. Li, C., Yang, S.: A generalized approach to construct benchmark problems for dynamic optimization. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 391–400. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Liang, J.J., Suganthan, P.N., Deb, K.: Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE, Swarm Intelligence Symposium, SIS 2005, pp. 68–75. (2005)

    Google Scholar 

  23. Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.-G., Suganthan, P.N.: Benchmark Generator for CEC 2009 Competition on Dynamic Optimization (2008)

    Google Scholar 

  24. Morrison, R.W.: Performance measurement in dynamic environments. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 5–8 (2003)

    Google Scholar 

  25. Yang, S., Nguyen, T.T., Li, C.: Evolutionary dynamic optimization: test and evaluation environments. In: Yang, S., Yao, X. (eds.) Evolutionary Computation for DOPs. SCI, vol. 490, pp. 3–37. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  26. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J. Heuristics 15, 617–644 (2009)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, (UNSW@ADFA), Canberra, 2600, Australia

    AbdelMonaem F. M. AbdAllah, Daryl L. Essam & Ruhul A. Sarker

Authors
  1. AbdelMonaem F. M. AbdAllah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daryl L. Essam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruhul A. Sarker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to AbdelMonaem F. M. AbdAllah .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

AbdAllah, A.F.M., Essam, D.L., Sarker, R.A. (2016). Solving Dynamic Optimisation Problems with Known Changeable Boundaries. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28270-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation