Skip to main content
SpringerLink
Log in
Book cover

Harmony Search Algorithm pp 11–17Cite as

Performance Measures of Metaheuristic Algorithms

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 382))

Abstract

Generally speaking, it is not fully understood why and how metaheuristic algorithms work very well under what conditions. It is the intention of this paper to clarify the performance characteristics of some of popular algorithms depending on the fitness landscape of specific problems. This study shows the performance of each considered algorithm on the fitness landscapes with different problem characteristics. The conclusions made in this study can be served as guidance on selecting algorithms to the problem of interest.

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 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barr, R., Golden, B., Kelly, J.: Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics 1(1), 9–32 (1995)

    Article  MATH  Google Scholar 

  2. Merz, P.: Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. Evolutionary Computation, IEEE 4(4), 337–352 (2000)

    Article  MathSciNet  Google Scholar 

  3. Yuan, B., Gallagher, M.: On building a principled framework for evaluating and testing evolutionary algorithms: a continuous landscape generator. In: Proceedings of the 2003 Congress on Evolutionary Computation, pp. 451–458. IEEE, Canberra (2003)

    Google Scholar 

  4. Yuan, B., Gallagher, M.: Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 172–181. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Gallagher, M., Yuan, B.: A General-purpose tunable landscape generator. IEEE Transactions on Evolutionary Computation (2006) (to appear)

    Google Scholar 

  6. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  7. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  8. Eskandar, H., Sadollah, A., Bahreininejad, A., Hamdi, M.: Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures 110, 151–166 (2012)

    Article  Google Scholar 

  9. Golberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addion Wesley (1989)

    Google Scholar 

  10. Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  11. 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 

  12. Kim, J.H., Geem, Z.W., Kim, E.S.: Parameter estimation of the nonlinear Muskingum model using harmony search. Journal of the American Water Resources Association 37(5), 1131–1138 (2001)

    Article  Google Scholar 

  13. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214. IEEE (2009)

    Google Scholar 

  14. Bäck, T.: Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press (1996)

    Google Scholar 

  15. Digalakis, J.G., Margaritis, K.G.: An experimental study of benchmarking functions for genetic algorithms. International Journal of Computer Mathematics 79(4), 403–416 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  16. Im, S.S., Yoo, D.G., Kim, J.H.: Smallest-small-world cellular harmony search for optimization of unconstrained benchmark problems. Journal of Applied Mathematics (2013)

    Google Scholar 

  17. Milad, A.: Harmony search algorithm: strengths and weaknesses. Journal of Computer Engineering and Information Technology (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 136-713, South Korea

    Joong Hoon Kim & Ho Min Lee

  2. Research Center for Disaster Prevention Science and Technology, Korea University, Seoul, 136-713, South Korea

    Donghwi Jung & Ali Sadollah

Authors
  1. Joong Hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ho Min Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donghwi Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Sadollah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joong Hoon Kim .

Editor information

Editors and Affiliations

  1. Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  2. Energy IT, Gachon University, Seongnam-Si, Korea (Republic of)

    Zong Woo Geem

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kim, J.H., Lee, H.M., Jung, D., Sadollah, A. (2016). Performance Measures of Metaheuristic Algorithms. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-47926-1_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47925-4

  • Online ISBN: 978-3-662-47926-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation