Skip to main content
SpringerLink
Log in

4-Rule Harmony Search Algorithm for Solving Computationally Expensive Optimization Test Problems

  • Conference paper
  • First Online:
Book cover Advances in Harmony Search, Soft Computing and Applications (ICHSA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1063))

Included in the following conference series:

Abstract

This paper proposes an enhanced harmony search algorithm for solving computationally expensive benchmarks widely used in the literature. We explored the potential and applicability of the original harmony search (HS) algorithm through introducing an extended version of the algorithm integrated with a new dynamic search equation enabling the algorithm to take guided larger steps at the beginning of the search. In the 4-Rule Harmony Search (4RHS), an extra rule is added to the standard HS without adding any user parameters to existing initial parameters. The 4RHS algorithm is then tested through optimal solving of different well-known and well-used benchmarks from classical to so CEC’ 2015 series, where the results of the 4RHS are compared with simple and improved version of HS algorithms as well as other optimization techniques. The obtained optimization results show the attractiveness of the added rule into the standard HS.

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 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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Kim, J.H., Geem, Z.W., Kim, E.: Parameter estimation of the nonlinear Muskingum model using harmony search. J. Am. Water Resour. Assoc. 37(5), 1131–1138 (2001)

    Article  Google Scholar 

  3. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)

    MathSciNet  MATH  Google Scholar 

  4. Geem, Z.W., Sim, K.B.: Parameter-setting-free harmony search algorithm. Appl. Math. Comp. 217(8), 3881–3889 (2010)

    Article  MathSciNet  Google Scholar 

  5. Gao, X.Z., Wang, X., Ovaska, S.J.: Uni-modal and multi-modal optimization using modified harmony search methods. Int. J. Innov. Comput. Inf. Control 5(10A), 2985–2996 (2009)

    Google Scholar 

  6. Manjarres, D., Landa-Torres, I., Gil-Lopez, S., DelSer, J., Bilbao, M.N., Salcedo-Sanz, S., Geem, Z.W.: A survey on applications of the harmony search algorithm. Eng. Appl. Artif. Intell. 26, 1818–1831 (2013)

    Article  Google Scholar 

  7. Fesanghary, M., Damangir, E., Soleimani, I.: Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm. Appl. Therm. Eng. 29(5–6), 1026–1031 (2009)

    Article  Google Scholar 

  8. Yoo, D.G., Kim, J.H., Geem, Z.W.: Overview of harmony search algorithm and its applications in civil engineering. Evol. Intell. 7, 3–16 (2014)

    Article  Google Scholar 

  9. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)

    Google Scholar 

  10. Zhang, P., Ouyang, H., Gao, L.: Improved harmony search algorithm with perturbation strategy. In: 27th Chinese Control and Decision Conference (CCDC) (2015). https://doi.org/10.1109/CCDC.2015.7162873

  11. Chen, Q., Liu, B., Zhang, Q., Liang, J.J., Suganthan, P.N., Qu, B.Y.: Problem definition and evaluation criteria for CEC 2015 special session and competition on bound constrained single-objective computationally expensive numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Nanyang Technological University, Singapore, Technical report (2014)

    Google Scholar 

  12. Andersson, M., Bandaru, S., Ng, A., Syberfeldt, A.: Parameter tuned CMA-ES on the CEC’15 expensive problems. In: IEEE CEC Evolutionary Computation, pp. 1950–1957 (2015)

    Google Scholar 

Download references

Acknowledgement

This work was supported by the National Research Foundation (NRF) of Korea under a grant funded by the Korean government (MSIP) (NRF-2019R1A2B5B03069810).

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Science and Culture, Tehran, Iran

    Ali Sadollah

  2. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, South Korea

    Joong Hoon Kim

  3. Research Center for the Disaster and Science Technology, Korea University, Seoul, South Korea

    Young Hwan Choi

  4. Department of Chemistry, Semnan University, Semnan, Iran

    Negar Karamoddin

Authors
  1. Ali Sadollah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joong Hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Young Hwan Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Negar Karamoddin
    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. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  2. Department of Energy IT , Gachon University, Seongnam, Kwangju-jikhalsi, Korea (Republic of)

    Zong Woo Geem

  3. Keimyung University, Daegu, Korea (Republic of)

    Donghwi Jung

  4. Department of Civil Engineering, Suwon University, Gyeonggi-do, Korea (Republic of)

    Do Guen Yoo

  5. Department of Mathematics, Dr. BR Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sadollah, A., Kim, J.H., Choi, Y.H., Karamoddin, N. (2020). 4-Rule Harmony Search Algorithm for Solving Computationally Expensive Optimization Test Problems. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_23

Download citation

Publish with us

Policies and ethics

Search

Navigation