Advertisement

A Sensitivity Analysis for Harmony Search with Multi-Parent Crossover Algorithm

Conference paper
  • 713 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1037)

Abstract

Harmony search algorithm with multi-parent crossover (HSA-MPC) is a hybrid algorithm that relies on benefiting from the crossover operation to combine more than one harmony to generate a new harmony. The picked harmonies are taken from an archive pool with best harmonies. In a previous study, the algorithm proves its efficiency when compared to other harmony search algorithms. In this paper, we will study the effect of harmony memory size (HMS), harmony memory consideration rate (HMCR), multi-parent crossover rate (MPCR), and the archive pool size on the quality of the generated solution. Eleven different scenarios are evaluated using a set of eight real-world numerical optimization problems introduced for CEC 2011 evolutionary algorithm competition. The analysis provides fixed values for all operators except the one under investigation. The obtained results prove the sensitivity of the algorithm to these operators and suggest a set of recommendations to improve the algorithm performance.

Keywords

Harmony search algorithm Evolutionary algorithms Numerical optimization Hybrid harmony search algorithm 

Notes

Acknowledgment

This research project is funded by the Dartmouth College and American University of Kuwait (Dartmouth-AUK) fellowship program.

References

  1. 1.
    Doush, I.A.: Harmony search with multi-parent crossover for solving IEEE-CEC2011 competition problems. In: Proceedings of the 19th International Conference on Neural Information Processing - Volume Part IV, ICONIP 2012, pp. 108–114 (2012)Google Scholar
  2. 2.
    Doush, I.A., Alkhateeb, F., Al Maghayreh, E., Al-Betar, M.A., Hasan, B.H.F.: Hybridizing harmony search algorithm with multi-parent crossover to solve real world optimization problems. Int. J. Appl. Metaheuristic Comput. 4(3), 1–14 (2013)CrossRefGoogle Scholar
  3. 3.
    Al-Betar, M.A., Khader, A.T.: A harmony search algorithm for university course timetabling. Ann. Oper. Res. 1–29 (2010).  https://doi.org/10.1007/s10479-010-0769-zMathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    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)zbMATHGoogle Scholar
  5. 5.
    Das, S., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical report, Nanyang Technological University, Singapore (2011)Google Scholar
  6. 6.
    Geem, Z.W.: Harmony search applications in industry. Soft Comput. Appl. Ind. 226, 117–134 (2008)Google Scholar
  7. 7.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  8. 8.
    Guo, Z., Yang, H., Wang, S., Zhou, C., Liu, X.: Adaptive harmony search with best-based search strategy. Soft Comput. 22(4), 1335–1349 (2018)CrossRefGoogle Scholar
  9. 9.
    Hasan, B.H.F., Doush, I.A., Al Maghayreh, E., Alkhateeb, F., Hamdan, M.: Hybridizing harmony search algorithm with different mutation operators for continuous problems. Appl. Math. Comput. 232(0), 1166–1182 (2014)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Ingram, G., Zhang, T.: Overview of applications and developments in the harmony search algorithm. In: Geem, Z.W. (ed.) Music-Inspired Harmony Search Algorithm. SCI, vol. 191, pp. 15–37. Springer, Heidelberg (2009)Google Scholar
  11. 11.
    Moon, Y.Y., Geem, Z.W., Han, G.-T.: Vanishing point detection for self-driving car using harmony search algorithm. Swarm Evol. Comput. 41, 111–119 (2018)CrossRefGoogle Scholar
  12. 12.
    Omran, M.G.H., Mahdavi, M.: Global-best harmony search. Appl. Math. Comput. 198(2), 643–656 (2008)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Sadollah, A., Sayyaadi, H., Yoo, D.G., Lee, H.M., Kim, J.H.: Mine blast harmony search: a new hybrid optimization method for improving exploration and exploitation capabilities. Appl. Soft Comput. 68, 548–564 (2018)CrossRefGoogle Scholar
  14. 14.
    Sawalha, R., Doush, I.A.: Face recognition using harmony search-based selected features. Int. J. Hybrid Inf. Technol. 5(2), 1–16 (2012)Google Scholar
  15. 15.
    Taleizadeh, A.A., Niaki, S.T.A., Barzinpour, F.: Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: a harmony search algorithm. Appl. Math. Comput. 217(22), 9234–9253 (2011)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Turky, A.M., Abdullah, S.: A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf. Sci. 272(C), 84–95 (2014)CrossRefGoogle Scholar
  17. 17.
    Wang, L., Hu, H., Liu, R., Zhou, X.: An improved differential harmony search algorithm for function optimization problems. Soft Comput. 23, 4827–4852 (2018)CrossRefGoogle Scholar
  18. 18.
    Wang, L., Pan, Q.-K., Fatih Tasgetiren, M.: A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem. Comput. Ind. Eng. 61(1), 76–83 (2011)CrossRefGoogle Scholar
  19. 19.
    Yi, J., Gao, L., Li, X., Gao, J.: An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems. Appl. Intell. 44(3), 725–753 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and Information Systems DepartmentAmerican University of KuwaitSalmiyaKuwait
  2. 2.Thayer Engineering SchoolDartmouth CollegeHanoverUSA

Personalised recommendations