Skip to main content
SpringerLink
Log in

A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5572))

Included in the following conference series:

Abstract

Dynamic optimisation problems are difficult to solve because they involve variables that change over time. In this paper, we present a new Hooke-Jeeves based Memetic Algorithm (HJMA) for dynamic function optimisation, and use the Moving Peaks (MP) problem as a test bed for experimentation. The results show that HJMA outperforms all previously published approaches on the three standardised benchmark scenarios of the MP problem. Some observations on the behaviour of the algorithm suggest that the original Hooke-Jeeves algorithm is surprisingly similar to the simple local search employed for this task in previous work.

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 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. Weise, T., Zapf, M., Chiong, R., Nebro, A.J.: Why is optimization difficult? In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol. 193, pp. 1–50. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Branke, J.: The moving peaks benchmark, http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/ (viewed 08/11/2008)

  3. Moser, I., Hendtlass, T.: A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, pp. 252–259 (2007)

    Google Scholar 

  4. Moser, I.: Applying extremal optimisation to dynamic optimisation problems. Ph.D. thesis, Faculty of Information and Communication Technologies, Swinburne University of Technology, Australia (2008)

    Google Scholar 

  5. Hooke, R., Jeeves, T.: Direct search solutions of numerical and statistical problems. Journal of the Association for Computing Machinery 8, 212–229 (1961)

    Article  MATH  Google Scholar 

  6. Boettcher, S., Percus, A.G.: Extremal optimization: methods derived from co-evolution. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, USA, pp. 825–832 (1999)

    Google Scholar 

  7. Branke, J.: Memory-enhanced evolutionary algorithms for changing optimization problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC 1999), Washington, DC, USA, pp. 1875–1882 (1999)

    Google Scholar 

  8. Branke, J., Kaußler, T., Schmidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Parmee, I.C. (ed.) Adaptive Computing in Design and Manufacturing (ACDM 2000), pp. 299–308. Springer, Berlin (2000)

    Google Scholar 

  9. Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui, S., Ghosh, A. (eds.) Theory and Application of Evolutionary Computation: Recent Trends, pp. 239–362. Springer, Berlin (2002)

    Google Scholar 

  10. Kramer, G.R., Gallagher, J.C.: Improvements to the *CGA enabling online intrinsic. In: Proc. NASA/DoD Conference on Evolvable Hardware, Chicago, Illinois, USA, pp. 225–231 (2003)

    Google Scholar 

  11. Zou, X., Wang, M., Zhou, A., Mckay, B.: Evolutionary optimization based on chaotic sequence in dynamic environments. In: Proc. IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, pp. 1364–1369 (2004)

    Google Scholar 

  12. Ronnewinkel, C., Martinetz, T.: Explicit speciation with few a priori parameters for dynamic optimization problems. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, San Francisco, California, USA, pp. 31–34 (2001)

    Google Scholar 

  13. Bui, L.T., Branke, J., Abbass, H.A.: Diversity as a selection pressure in dynamic environments. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, DC, USA, pp. 1557–1558 (2005)

    Google Scholar 

  14. Bui, L.T., Branke, J., Abbass, H.A.: Multiobjective optimization for dynamic environments. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, pp. 2349–2356 (2005)

    Google Scholar 

  15. Fentress, S.W.: Exaptation as a means of evolving complex solutions. Master’s Thesis, School of Informatics, University of Edinburgh, UK (2005)

    Google Scholar 

  16. Blackwell, T.M.: Swarms in dynamic environments. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2003), Chicago, Illinois, USA, pp. 1–12 (2003)

    Google Scholar 

  17. Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  18. Parrott, D., Li, X.: A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2004), Portland, Oregon, USA, pp. 105–116 (2004)

    Google Scholar 

  19. Janson, S., Middendorf, M.: A hierachical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  20. Wang, H., Wang, D., Yang, S.: Triggered memory-based swarm optimisation in dynamic environments. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 637–646. Springer, Heidelberg (2007)

    Google Scholar 

  21. Blackwell, T., Branke, J.: Multi-swarms, exclusion and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10(4), 51–58 (2006)

    Article  Google Scholar 

  22. Mendes, R., Mohais, A.: DynDE: a differential evolution for dynamic optimization problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, pp. 2808–2815 (2005)

    Google Scholar 

  23. Meyer, K.D., Nasut, S.J., Bishop, M.: Stochastic diffusion search: partial function evaluation in swarm intelligence dynamic optimization. In: Abraham, A., et al. (eds.) Stigmergic Optimization. Studies in Computational Intelligence, vol. 31, pp. 185–207. Springer, Berlin (2006)

    Chapter  Google Scholar 

  24. Trojanowski, K.: B-cell algorithm as a parallel approach to optimization of moving peaks benchmark tasks. In: Proc. 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2007), Elk, Poland, pp. 143–148 (2007)

    Google Scholar 

  25. Lung, R.I., Dumitrescu, D.: A collaborative model for tracking optima in dynamic environments. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, pp. 564–567 (2007)

    Google Scholar 

  26. Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Physics Review Letters 74, 4083–4086 (1993)

    Article  Google Scholar 

  27. Boettcher, S., Percus, A.G.: Nature’s way of optimizing. Artificial Intelligence 119(1), 275–286 (2000)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of ICT, Swinburne University of Technology, Melbourne, Australia

    Irene Moser

  2. School of Computing & Design, Swinburne University of Technology (Sarawak Campus), Jalan Simpang Tiga, 93350, Kuching, Sarawak, Malaysia

    Raymond Chiong

Authors
  1. Irene Moser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raymond Chiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  2. Department of Computer Science, University of Vermont, 33 Colchester Avenue, Burlington, VT, USA

    Xindong Wu

  3. Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Grupo de Investigación GICAP, Área de Lenguajes y Sistemas Informáticos, Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Campus Vena, Francisco de Vitoria, 09006, Burgos, Spain

    Álvaro Herrero  & Bruno Baruque  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Moser, I., Chiong, R. (2009). A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02319-4_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02318-7

  • Online ISBN: 978-3-642-02319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation