Multi-layered Harmony Search Algorithm: Introduction of a Novel and Efficient Structure

  • Ho Min Lee
  • Do Guen Yoo
  • Eui Hoon Lee
  • Young Hwan Choi
  • Joong Hoon KimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 514)


The harmony search algorithm (HSA) is one of the most widely used meta-heuristic optimization algorithms. During the last two decades, many improved versions and variants of HSA have been proposed to improve the algorithms efficiency and usability. In this study, a HSA variant with unique structural characteristics is proposed, named multi-layered harmony search algorithm (MLHSA). The multi-layered structure is specifically designed for the effective improvement of exploration and exploitation capability. The proposed MLHSA is applied to a set of benchmark problems to test and verify the efficiency. The application results show that MLHSA outperforms other meta-heuristic algorithms, indicating the competitiveness of the algorithm. The multi-layer concept can be easily employed to other algorithms, as a helpful tool for the improvement of existing algorithms convergence.


Optimization Meta-heuristic Harmony search algorithm Multi-layered harmony search algorithm 



This research was supported by a grant from The National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) under no. 2016R1A2A1A05005306.


  1. 1.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)CrossRefGoogle Scholar
  2. 2.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  3. 3.
    Im, S.S., Yoo, D.G., Kim, J.H.: Smallest-small-world cellular harmony search for optimization of unconstrained benchmark problems. J. Appl. Math. 2013 (2013). Article ID: 635608Google Scholar
  4. 4.
    Al-Betar, M.A., Khader, A.T., Awadallah, M.A., Alawan, M.H., Zaqaibeh, B.: Cellular harmony search for optimization problems. J. Appl. Math. 2013 (2013). Article ID: 139464Google Scholar
  5. 5.
    Reca, J., Martínez, J.: Genetic algorithms for the design of looped irrigation water distribution networks. Water Resour. Res. 42(5) (2006). Article ID: W05416Google Scholar
  6. 6.
    Reca, J., Martínez, J., Gil, C., BaÃśos, R.: Application of several meta-heuristic techniques to the optimization of real looped water distribution networks. Water Resour. Manage. 22(10), 1367–1379 (2007)CrossRefGoogle Scholar
  7. 7.
    Geem, Z.W.: Particle-swarm harmony search for water network design. Eng. Optim. 41(4), 297–311 (2009)CrossRefGoogle Scholar
  8. 8.
    Bolognesi, A., Bragalli, C., Marchi, A., Artina, S.: Genetic heritage evolution by stochastic transmission in the optimal design of water distribution networks. Adv. Eng. Softw. 41(5), 792–801 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Sadollah, A., Yoo, D.G., Kim, J.H.: Improved mine blast algorithm for optimal cost design of water distribution systems. Eng. Optim. 47(12), 1602–1618 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Ho Min Lee
    • 1
  • Do Guen Yoo
    • 2
  • Eui Hoon Lee
    • 1
  • Young Hwan Choi
    • 1
  • Joong Hoon Kim
    • 1
    Email author
  1. 1.School of Civil, Environmental and Architectural EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Software Development and Engineering Department, Software Center, K-water Research InstituteKorea Water Resources Corporation (K-water)DaejeonSouth Korea

Personalised recommendations