A Modified Harmony Search Threshold Accepting Hybrid Optimization Algorithm

  • Yeturu Maheshkumar
  • Vadlamani Ravi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)


Hybrid metaheuristics are the recent trend that caught the attention of several researchers which are more efficient than the metaheuristics in finding the global optimal solution in terms of speed and accuracy. This paper presents a novel optimization metaheuristic by hybridizing Modified Harmony Search (MHS) and Threshold Accepting (TA) algorithm. This methodology has the advantage that one metaheuristic is used to explore the entire search space to find the area near optima and then other metaheuristic is used to exploit the near optimal area to find the global optimal solution. In this approach Modified Harmony Search was employed to explore the search space whereas Threshold Accepting algorithm was used to exploit the search space to find the global optimum solution. Effectiveness of the proposed hybrid is tested on 22 benchmark problems. It is compared with the recently proposed MHS+MGDA hybrid. The results obtained demonstrate that the proposed methodology outperforms the MHS and MHS+MGDA in terms of accuracy and functional evaluations and can be an expeditious alternative to MHS and MHS+MGDA.


Harmony Search Threshold Accepting Hybrid Metaheuristic Unconstrained Optimization Metaheuristic 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Choudhuri, R., Ravi, V., Mahesh Kumar, Y.: A Hybrid Harmony Search and Modified Great Deluge Algorithm for Unconstrained Optimization. Int. Jo. of Comp. Intelligence Research 6(4), 755–761 (2010)Google Scholar
  2. 2.
    Dueck, G., Scheur, T.: Threshold Accepting: A General Purpose Optimization Algorithm appearing Superior to Simulated Annealing. Jo. of Comp. Physics 90, 161–175 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Edmund, K.B., Graham, K.: Search Methodologies: Introductory Tutorials in Optimization and Decission Support Techniques. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  4. 4.
    Glover, F.: Future Paths for Integer Programming and Links to Artificial Intelligence. Computers and Op. Research 13(5), 533–549 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Geem, Z., Kim, J., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76, 60–68 (2001)CrossRefGoogle Scholar
  6. 6.
    Ravi, V., Murthy, B.S.N., Reddy, P.J.: Non-equilibrium simulated annealing-algorithm applied to reliability optimization of complex systems. IEEE Trans. on Reliability 46, 233–239 (1997)CrossRefGoogle Scholar
  7. 7.
    Trafalis, T.B., Kasap, S.: A novel metaheuristics approach for continuous global optimization. Jo. of Global Optimization 23, 171–190 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Chelouah, R., Siarry, P.: Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multi-minima functions. European Jo. of Op. Research 148, 335–348 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Schimdt, H., Thierauf, G.: A Combined Heuristic Optimization Technique. Advance in Engineering Software 36(1), 11–19 (2005)CrossRefzbMATHGoogle Scholar
  10. 10.
    Bhat, T.R., Venkataramani, D., Ravi, V., Murty, C.V.S.: Improved differential evolution method for efficient parameter estimation in biofilter modeling. Biochemical Eng. Jo. 28, 167–176 (2006)CrossRefGoogle Scholar
  11. 11.
    Srinivas, M., Rangaiah, G.: Differential Evolution with Tabu list for Global Optimization and its Application to Phase Equilibrium and Parameter Estimation. Problems Ind. Engg. Chem. Res. 46, 3410–3421 (2007)CrossRefGoogle Scholar
  12. 12.
    Chauhan, N., Ravi, V.: Differential Evolution and Threshold Accepting Hybrid Algorithm for Unconstrained Optimization. Int. Jo. of Bio-Inspired Computation 2, 169–182 (2010)CrossRefGoogle Scholar
  13. 13.
    Li, H., Li, L.: A novel hybrid particle swarm optimization algorithm combined with harmony search for higher dimensional optimization problems. In: Int. Conference on Intelligent Pervasive Computing, Jeju Island, Korea (2007)Google Scholar
  14. 14.
    Fesanghary, M., Mahdavi, M., Joldan, M.M., Alizadeh, Y.: Hybridizing harmony search algorithm with sequential programming for engineering optimization problems. Comp. Methods Appl. Mech. Eng. 197, 3080–3091 (2008)CrossRefzbMATHGoogle Scholar
  15. 15.
    Gao, X.Z., Wang, X., Ovaska, J.: Uni-Modal and Multi Modal optimization using modified harmony search methods. IJICIC 5(10(A)), 2985–2996 (2009)Google Scholar
  16. 16.
    Kaveh, A., Talatahari, S.: PSO, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers and Structures 87, 267–283 (2009)CrossRefGoogle Scholar
  17. 17.
    Ravi, V.: Optimization of Complex System Reliability by a Modified Great Deluge Algorithm. Asia-Pacific Jo. of Op. Research 21(4), 487–497 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ali, M.M., Charoenchai, K., Zelda, B.Z.: A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems. Jo. of Global Optimization 31, 635–672 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Aluffi-Pentini, F., Parisi, V., Zirilli, F.: Global optimization and stochastic differential equations. Jo. of Op. Theory and Applications 47, 1–16 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Price, W.L.: Global Optimization by Controlled Random Search. Computer Jo. 20, 367–370 (1977)CrossRefzbMATHGoogle Scholar
  21. 21.
    Bohachevsky, M.E., Johnson, M.E., Stein, M.L.: Generalized simulated annealing for function optimization. Techno Metrics 28, 209–217 (1986)CrossRefzbMATHGoogle Scholar
  22. 22.
    Dixon, L., Szego, G.: Towards Global Optimization 2. North Holland, New York (1978)Google Scholar
  23. 23.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)CrossRefzbMATHGoogle Scholar
  24. 24.
    Dekkers, A., Aarts, E.: Global optimization and simulated annealing. Mathematical Programming 50, 367–393 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Wolfe, M.A.: Numerical Methods for Unconstrained Optimization. Van Nostrand Reinhold Company, New York (1978)zbMATHGoogle Scholar
  26. 26.
    Salomon, R.: Reevaluating Genetic Algorithms Performance under Co-ordinate Rotation of Benchmark Functions. Bio. Systems 39(3), 263–278 (1995)CrossRefGoogle Scholar
  27. 27.
    Muhlenbein, H., Schomisch, S., Born, J.: The parallel genetic algorithm as function optimizer. In: Belew, R., Booker, L. (eds.) Proceedings of the Fourth Int. Conference on Genetic Algorithms, pp. 271–278. Morgan Kaufmann (1991)Google Scholar
  28. 28.
    Sphere problem; global and local optima, (cited on November 20, 2010)
  29. 29.
    Zakharov Problem Global and local optima, (cited on November 20, 2010)

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yeturu Maheshkumar
    • 1
    • 2
  • Vadlamani Ravi
    • 1
  1. 1.Institute for Development and Research in Banking TechnologyHyderabadIndia
  2. 2.Department of Computer & Information SciencesUniversity of HyderabadHyderabadIndia

Personalised recommendations