Incorporating Great Deluge with Harmony Search for Global Optimization Problems

  • Mohammed Azmi Al-Betar
  • Osama Nasif Ahmad
  • Ahamad Tajudin  Khader
  • Mohammed A. Awadallah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 201)


Harmony search (HS) algorithm is relatively a recent metaheuristic optimization method inspired by natural phenomenon of musical improvisation process. Despite its success, the main drawback of harmony search are contained in its tendency to converge prematurely due to its greedy selection method. This probably leads the harmony search algorithm to get stuck in local optima and unsought solutions owing to the limited exploration of the search space. The great deluge algorithm is a local search-based approach that has an efficient capability of increasing diversity and avoiding the local optima. This capability comes from its flexible method of accepting the new constructed solution. The aim of this research is to propose and evaluate a new variant of HS. To do so, the acceptance method of the great deluge algorithm is incorporated in the harmony search to enhance its convergence properties by maintaining a higher rate of diversification at the initial stage of the search process. The proposed method is called Harmony Search Great Deluge (HS-GD) algorithm. The performance of HS-GD and the classical harmony search algorithm was evaluated using a set of ten benchmark global optimization functions. In addition, five benchmark functions of the former set were employed to compare the results of the proposed method with three previous harmony search variations including the classical harmony search. The results show that HS-GD often outperforms the other comparative approaches.


Harmony search Great deluge Global optimization Diversification Intensification 


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  1. M.S. Abual-Rub, M.A. Al-Betar, R. Abdullah, and A.T. Khader. A hybrid harmony search algorithm for ab initio protein tertiary structure prediction. Network Modeling and Analysis in Health Informatics and, Bioinformatics, pages 1-17.Google Scholar
  2. M. A. Al-Betar, A. T. Khader, and F. Nadi. Selection mechanisms in memory consideration for examination timetabling with harmony search. In GECCO ’10: Proceedings of Genetic and Evolutionary Computation Conference. ACM, Portland, Oregon, USA, July 7-11 2010.Google Scholar
  3. M. A. Al-Betar, A. T. Khader, and J. J. Thomas. A combination of metaheuristic components based on harmony search for the uncapacitated examination timetabling. In 8th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2010), Belfast, Northern Ireland, August 10-13 2010.Google Scholar
  4. M.A. Al-Betar, I.A. Doush, A.T. Khader, and M.A. Awadallah. Novel selection schemes for harmony search. Applied Mathematics and Computation, 218(10), 2011.Google Scholar
  5. M.A. Al-Betar and A.T. Khader. A harmony search algorithm for university course timetabling. Annals of Operations Research, 194:1-29, 2012.Google Scholar
  6. M.A. Al-Betar, A.T. Khader, and M. Zaman. University course timetabling using a hybrid harmony search metaheuristic algorithm. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, (99):1-18.Google Scholar
  7. O. Alia, M. Al-Betar, R. Mandava, A. Khader. Data clustering using harmony search algorithm. Swarm, Evolutionary, and, Memetic Computing, pages 79-88, 2011.Google Scholar
  8. M. Awadallah, A. Khader, M. Al-Betar, A. Bolaji. Nurse rostering using modified harmony search algorithm. Swarm, Evolutionary, and, Memetic Computing, pages 27-37, 2011.Google Scholar
  9. M.A. Awadallah, A.T. Khader, M.A. Al-Betar, and A.L. Bolaji. Nurse scheduling using harmony search. In Bio-Inspired Computing: Theories and Applications (BIC-TA), 2011 Sixth International Conference on, pages 58-63. IEEE, 2011.Google Scholar
  10. Christian Blum and Andrea Roli. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv., 35(3):268-308, 2003.Google Scholar
  11. P. Chakraborty, G.G. Roy, S. Das, D. Jain, and A. Abraham. An improved harmony search algorithm with differential mutation operator. Fundamenta Informaticae, 95(4):401-426, 2009.Google Scholar
  12. G. Dueck. New optimization heuristics. Journal of computational physics, 104(1):86-92, 2005.Google Scholar
  13. Z. Geem. State-of-the-art in the structure of harmony search algorithm. Recent Advances In Harmony Search Algorithm, pages 1-10, 2010.Google Scholar
  14. Z. W. Geem, J. H. Kim, and G. V. Loganathan. A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2):60-68, 2001.Google Scholar
  15. Z.W. Geem, M. Fesanghary, J. Choi, MP Saka, J.C.Williams, M.T. Ayvaz, L. Li, S. Ryu, and A. Vasebi. Recent advances in harmony search. Advance in evolutionary algorithms, I-Teach Education and Publishing, Vienna, Austria, pages 127-142, 2008.Google Scholar
  16. M. G. H. Omran and M. Mahdavi. Global-best harmony search. Applied Mathematics and Computation, 198(2):643-656, 2008.Google Scholar
  17. Quan-Ke Pan, P.N. Suganthan, M. Fatih Tasgetiren, and J.J. Liang. A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216(3):830 -848, 2010.Google Scholar
  18. AK Qin and F. Forbes. Dynamic regional harmony search with opposition and local learning. In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, pages 53-54. ACM, 2011.Google Scholar
  19. C.M.Wang and Y.F. Huang. Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications, 37(4):2826-2837, 2010.Google Scholar
  20. Xin Yao, Yong Liu, and Guangming Lin. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2):82-102, 1999.Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Mohammed Azmi Al-Betar
    • 1
    • 2
  • Osama Nasif Ahmad
    • 2
  • Ahamad Tajudin  Khader
    • 2
  • Mohammed A. Awadallah
    • 2
  1. 1.Department of Computer ScienceJadara UniversityIrbidJordan
  2. 2.School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia

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