Harmony Search with Multi-Parent Crossover for Solving IEEE-CEC2011 Competition Problems

  • Iyad Abu Doush
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7666)


Harmony search algorithm (HSA) is a recent evolutionary algorithm used to solve several optimization problems. The algorithm mimic the improvisation behaviour of a group of musicians to find a good harmony. Several variations of HSA has been proposed to enhance its performance. In this paper, a new variation of HSA that uses multi-parent crossover is proposed (HSA-MPC). In this technique three harmonies are used to generate three new harmonies that will replace the worst three solution vectors in the harmony memory (HM). The algorithm has been applied to solve a set of eight real world numerical optimization problems (1-8) introduced for IEEE-CEC2011 evolutionary algorithm competition. The experiemental results of the proposed algorithm is compared with the original HSA, and two variations of HSA: global best HSA and tournament HSA. The HSA-MPC almost always shows superiority on all test problems.


Harmony Search Evolutionary Algorithms Numerical Optimization 


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  1. 1.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A New Heuristic Optimization Algorithm: Harmony Search. Simul. 76, 60–68 (2001)CrossRefGoogle Scholar
  2. 2.
    Al-Betar, M.A., Khader, A.T.: A Harmony Search Algorithm for University Course Timetabling. Ann. Oper. Res., 1–29 (2010)Google Scholar
  3. 3.
    Geem, Z.W.: Harmony Search Applications in Industry. Soft Comput. Appl. Ind. 226, 117–134 (2008)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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, 9234–9253 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Wang, L., Pan, Q.K., Tasgetiren, M.F.: A Hybrid Harmony Search Algorithm for the Blocking Permutation Flow Shop Scheduling Problem. Comput. Ind. Eng. 61, 76–83 (2011)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley (1989)Google Scholar
  8. 8.
    Durand, N., Alliot, J.M.: Genetic Crossover Operator for Partially Separable Functions. In: Proceedings of the Third Annual Genetic Programming Conference (1998)Google Scholar
  9. 9.
    Goldberg, D., Deb, K., Korb, B.: Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Syst. 3, 493–530 (1989)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)Google Scholar
  11. 11.
    Al-Betar, M.A., Khader, A.T., Liao, I.Y.: A Harmony Search Algorithm with Multi-pitch Adjusting Rate for University Course Timetabling. In: Geem, Z.W. (ed.) Recent Advances In Harmony Search Algorithm, vol. 270, pp. 147–162. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Omran, M.G.H., Mahdavi, M.: Global-Best Harmony Search. Appl. Math. Comput. 198, 643–656 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Pan, Q.K., Suganthan, P.N., Tasgetiren, M.T., Liang, J.J.: A Self-adaptive Global Best Harmony Search Algorithm for Continuous Optimization Problems. Appl. Math. Comput. 216, 830–848 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Wang, C.M., Huang, Y.F.: Self-adaptive Harmony Search Algorithm for Optimization. Expert Syst. Appl. 37, 2826–2837 (2010)CrossRefGoogle Scholar
  15. 15.
    Elsayed, S.M., Sarker, R.A., Essam, D.L.: Ga with A New Multi-parent Crossover for Solving IEEE-CEC2011 Competition Problems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1034–1040 (2011)Google Scholar
  16. 16.
    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
  17. 17.
    Al-Betar, M.A., Doush, I.A., Khader, A.T., Awadallah, M.A.: Novel Selection Schemes for Harmony Search. Appl. Math. Comput. (2011)Google Scholar
  18. 18.
    Mahdavi, M., Fesanghary, M., Damangir, E.: An Improved Harmony Search Algorithm for Solving Optimization Problems. Appl. Math. Comput. 188, 1567–1579 (2007)MathSciNetzbMATHCrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Iyad Abu Doush
    • 1
  1. 1.Computer Science DepartmentYarmouk UniversityIrbidJordan

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