Mine Blast Harmony Search and Its Applications

  • Ali Sadollah
  • Ho Min Lee
  • Do Guen Yoo
  • Joong Hoon Kim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 382)


A hybrid optimization method that combines the power of the harmony search (HS) algorithm with the mine blast algorithm (MBA) is presented in this study. The resulting mine blast harmony search (MBHS) utilizes the MBA for exploration and the HS for exploitation. The HS is inspired by the improvisation process of musicians, while the MBA is derived based on explosion of landmines. The HS used in the proposed hybrid method is an improved version, introducing a new concept for the harmony memory (HM) (i.e., dynamic HM), while the MBA is modified in terms of its mathematical formulation. Several benchmarks with many design variables are used to validate the MBHS, and the optimization results are compared with other algorithms. The obtained optimization results show that the proposed hybrid algorithm provides better exploitation ability (particularly in final iterations) and enjoys fast convergence to the optimum solution.


Harmony search Mine blast algorithm Hybrid metaheuristic methods Global optimization Large-scale problems 


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  1. 1.
    Geem, G.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  2. 2.
    Kim, J.H., Geem, G.W., Kim, E.S.: Parameter estimation of the nonlinear Muskingum model using harmony search. J. of Amer. Wat. Res. Assoc. 37(5), 1131–1138 (2001)CrossRefGoogle Scholar
  3. 3.
    Geem, G.W., Kim, J.H., Loganathan, G.V.: Harmony search optimization: application to pipe network design. Int. J. Modelling Simul. 22(2), 125–133 (2002)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Degertekin, S.: Optimum design of steel frames using harmony search algorithm. Struct. Multidiscip. Optim. 36(4), 393–401 (2008)CrossRefGoogle Scholar
  6. 6.
    Geem, Z.W. (ed.): Harmony Search Algo. for Structural Design Optimization. SCI, vol. 293. Springer, Berlin (2009)Google Scholar
  7. 7.
    Kim, J.H., Baek, C.W., Jo, D.J., Kim, E.S., Park, M.J.: Optimal planning model for rehabilitation of water network. Water Sci. and Technol. Water Supply 4(3), 133–147 (2004)Google Scholar
  8. 8.
    Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Geem, Z.W., Sim, K.B.: Parameter-setting-free harmony search algorithm. Appl. Math. Comp. 217(8), 3881–3889 (2010)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P.M., Fei, M.: An improved adaptive binary Harmony Search algorithm. Inform. Sciences 232, 58–87 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kaveh, A., Talatahari, S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87(5–6), 267–283 (2009)CrossRefGoogle Scholar
  12. 12.
    Geem, Z.W.: Particle-swarm harmony search for water network design. Eng. Optim. 41(4), 297–311 (2009)CrossRefGoogle Scholar
  13. 13.
    Ayvaz, M.T., Kayhan, A.H., Ceylan, H., Gurarslan, G.: Hybridizing the harmony search algorithm with a spreadsheet ‘solver’ for solving continuous engineering optimization problems. Eng. Optim. 41(12), 1119–1144 (2009)CrossRefGoogle Scholar
  14. 14.
    Fesanghary, M., Mahdavi, M., Minary-Jolandan, M., Alizadeh, Y.: Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput. Methods Appl. Mech. Eng. 197(33–40), 3080–3091 (2008)zbMATHCrossRefGoogle Scholar
  15. 15.
    Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M.: Mine blast algorithm for optimization of truss structures with discrete variables. Comput. Struct. 102–103, 49–63 (2012)CrossRefGoogle Scholar
  16. 16.
    Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M.: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Appl. Soft Comput. 13(5), 2592–2612 (2013)CrossRefGoogle Scholar
  17. 17.
    Yadav, P., Kumar, R., Panda, S.K., Chang, C.S.: An intelligent tuned harmony search algorithm for optimization. Inf. Sciences 196, 47–72 (2012)CrossRefGoogle Scholar
  18. 18.
    Yang, H.O., Gao, L., Li, S., Kong, X., Zou, D.: On the iterative convergence of harmony search algorithm and a proposed modification. Appl. Math. Comput. 247(15), 1064–1095 (2014)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, vol. IV, pp. 1942–1948 (1995)Google Scholar
  20. 20.
    Atashpaz-Gargari, E.: Lucas, C: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE CEC 7, 4661–4666 (2007)Google Scholar
  21. 21.
    Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Inform. Sciences 179, 2232–2248 (2009)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ali Sadollah
    • 1
  • Ho Min Lee
    • 2
  • Do Guen Yoo
    • 1
  • Joong Hoon Kim
    • 2
  1. 1.Research Center for Disaster Prevention Science and TechnologyKorea UniversitySeoulSouth Korea
  2. 2.School of Civil, Environmental, and Architectural EngineeringKorea UniversitySeoulSouth Korea

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