Skip to main content
SpringerLink
Log in
Book cover

IAENG Transactions on Engineering Technologies pp 475–489Cite as

Fertilization Operator for Multi-Modal Dynamic Optimization

  • Chapter
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 229))

Abstract

Solving Multi-modal Dynamic Optimization problems (MDO) has been a challenge for genetic algorithms (GAs). In this kind of optimization, an algorithm requires not only to find the multiple optimal solutions but also to locate a changing optimum dynamically. To enhance the performance of GAs in MDO, this paper proposes a New Genetic Operator NGO. The NGO is built on three components. First, a novel Genetic Algorithm with Dynamic Niche Sharing (GADNS) which permits to encourage the speciation. Second, an unsupervised fuzzy clustering that tracks multiple optima and enhances GADNS. Third, Spacial Separation (SS) which induces the stable sub-populations and allows local competition. In addition, NGO maintains diversity by a new genetic operators. To control the selection pressure, a new tournament selection is presented. Moving Peaks benchmark is applied to test the performance of NGO. The ability of the NGO to track multiple optima is demonstrated by a new diversity measure.

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   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
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Bezdec J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  Google Scholar 

  2. Bouroumi A, Essaïdi A (2000) Unsupervised fuzzy learning and cluster seeking. Intell Data Anal 4(3):241–253

    Google Scholar 

  3. Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer Academic, Dordrecht

    Book  MATH  Google Scholar 

  4. Branke J, Schmidt L, Schmeck H (2000) A multi-population approach to dynamic optimization problems. In: Parmee IC (ed) 4th international conference on adaptive computing in design and manufacture (ACDM 2000). Springer, Berlin, pp 299–308

    Google Scholar 

  5. Cedeno W, Vemuri VR (2007) A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genet Program Evol Mach 8(3):255–286

    Article  Google Scholar 

  6. Cobb HG (1990) An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent non-stationary environments. TIK-report 6760, NLR memorandum, Naval Research Laboratory, Washington, DC, USA

    Google Scholar 

  7. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9(2):115–148

    MathSciNet  MATH  Google Scholar 

  8. Deb K, Kumar A (1995) Real-coded genetic algorithms with simulated binary crossover: studies on multimodal and multiobjective problems. Complex Syst 9(6):431–454

    Google Scholar 

  9. Goldberg DE, Wang L (1997) Adaptive niching via coevolutionary sharing. TIK-report 97007, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Avenue Urbana, IL 61801

    Google Scholar 

  10. Grefenstette JJ (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Autom Comput 4(3):243–254

    Article  Google Scholar 

  11. Jebari K, Bouroumi A, Ettouhami A (2012) New genetic operator for dynamic optimization. In: Lecture notes in engineering and computer science: proceedings of The world congress on engineering 2012, WCE 2012, London, UK, 4–6 July 2012, pp 742–747. http://www.iaeng.org/publication/WCE2012/

  12. Jebari K, Bouroumi A et al (2011) Unsupervised fuzzy tournament selection. Appl Math Sci 28(1):2863–2881

    MathSciNet  Google Scholar 

  13. Kuncheva LI, Bezdec JC (1998) Nearest prototype classification: clustering, genetic algorithms or random search. IEEE Trans Syst Man Cybernet 28(1):160–164

    Article  Google Scholar 

  14. Louis SJ, Xu Z (2008) An immigrants scheme based on environmental information for genetic algorithms in changing environments. In: The 2008 IEEE congress on evolutionary computation, Hong Kong. IEEE, pp 1141–1147

    Google Scholar 

  15. Miller BL, Shaw MJ (1995) Genetic algorithms with dynamic niche sharing for multimodal function optimization. TIK-report 95010, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Avenue, Urbana, IL 61801

    Google Scholar 

  16. Saäreni B, Krähenbühl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3):97–106

    Article  Google Scholar 

  17. Yang S, Yao S (2007) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5):542–561

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Conception and Systems laboratory, Faculty of Sciences, University (UM5A), Rabat, Morocco

    Khalid Jebari & Aziz Ettouhami

  2. Faculty of sciences Ben Msik, Hassan II Mohammadia-Casablanca University, Casablanca, Morocco

    Abdelaziz Bouroumi

Authors
  1. Khalid Jebari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelaziz Bouroumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aziz Ettouhami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khalid Jebari .

Editor information

Editors and Affiliations

  1. , Department of Multimedia Engineering, Mokpo National University, Dorim-ri, MuanGun 61, Chonnam, 534-729, Korea, Republic of (South Korea)

    Gi-Chul Yang

  2. IAENG Secretariat, Hung To Road, Unit 1, 1/F 37-39, Hong Kong, Hong Kong SAR

    Sio-long Ao

  3. School of Engineering, Applied Mathematics and Computing, Cranfield University, College Road, Cranfield, MK43 0AL, Bedfordshire, United Kingdom

    Len Gelman

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Jebari, K., Bouroumi, A., Ettouhami, A. (2013). Fertilization Operator for Multi-Modal Dynamic Optimization. In: Yang, GC., Ao, Sl., Gelman, L. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6190-2_36

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-6190-2_36

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6189-6

  • Online ISBN: 978-94-007-6190-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation