Skip to main content
SpringerLink
Log in
Book cover

International Conference on Neural Information Processing

ICONIP 2015: Neural Information Processing pp 606–614Cite as

Hybedrized NSGA-II and MOEA/D with Harmony Search Algorithm to Solve Multi-objective Optimization Problems

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

Abstract

A multi-objective optimization problem is an area concerned an optimization problem involving more than one objective function to be optimized simultaneously. Several techniques have been proposed to solve Multi-Objective Optimization Problems. The two most famous algorithms are: NSGA-II and MOEA/D. Harmony Search is relatively a new heuristic evolutionary algorithm that has successfully proven to solve single objective optimization problems. In this paper, we hybridized two well-known multi-objective optimization evolutionary algorithms: NSGA-II and MOEA/D with Harmony Search. We studied the efficiency of the proposed novel algorithms to solve multi-objective optimization problems. To evaluate our work, we used well-known datasets: ZDT, DTLZ and CEC2009. We evaluate the algorithm performance using Inverted Generational Distance (IGD). The results showed that the proposed algorithms outperform in solving problems with multiple local fronts in terms of IGD as compared to the original ones (i.e., NSGA-II and MOEA/D).

Dr. Iyad Abu Doush, Department of Computer Sciences, Yarmouk University, Zip Code 21163, Irbid, Jordan. Phone: 00962-2-7211111 ext: 3858, Fax: 00962-2-7211128.

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. Abraham, A., Jain, L.: Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London (2005)

    Book  MATH  Google Scholar 

  2. Al-Betar, M.A., Doush, I.A., Khader, A.T., Awadallah, M.A.: Novel selection schemes for harmony search. Appl. Math. Comput. 218(10), 6095–6117 (2012)

    MATH  Google Scholar 

  3. Bataineh, M.Q.: Hybridizing evolutionary multi-objective optimization algorithms with the harmony search algorithm. Master’s thesis, Yarmouk University, Irbid, Jordan, January 2015

    Google Scholar 

  4. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  6. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  7. Hasan, B.H.F., Doush, I.A., Maghayreh, E.A., Alkhateeb, F., Hamdan, M.: Hybridizing harmony search algorithm with different mutation operators for continuous problems. Appl. Math. Comput. 232, 1166–1182 (2014)

    MathSciNet  Google Scholar 

  8. Ingram, G., Zhang, T.: Overview of applications and developments in the harmony search algorithm. In: Geem, Z.W. (ed.) Music-Inspired Harmony Search Algorithm. Studies in Computational Intelligence, vol. 191, pp. 15–37. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Meth. Appl. Mech. Eng. 194(36), 3902–3933 (2005)

    Article  MATH  Google Scholar 

  10. Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)

    Article  Google Scholar 

  11. Sindhya, K., Miettinen, K., Deb, K.: A hybrid framework for evolutionary multi-objective optimization. IEEE Trans. Evol. Comput. 17(4), 495–511 (2013)

    Article  MATH  Google Scholar 

  12. Wang, L., Pan, Q.-K., Tasgetiren, M.F.: A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Comput. Ind. Eng. 61(1), 76–83 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Yarmouk University, Irbid, Jordan

    Iyad Abu Doush & Mohammad Qasem Bataineh

Authors
  1. Iyad Abu Doush
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Qasem Bataineh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iyad Abu Doush .

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

Doush, I.A., Bataineh, M.Q. (2015). Hybedrized NSGA-II and MOEA/D with Harmony Search Algorithm to Solve Multi-objective Optimization Problems. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_67

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26532-2_67

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation