Harmony Search with Teaching-Learning Strategy for 0-1 Optimization Problem

  • Longquan YongEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)


0-1 optimization problem plays an important role in operational research. In this paper, we use a recently proposed algorithm named harmony search with teaching-learning (HSTL) strategy which derived from Teaching-Learning-Based Optimization (TLBO) for solving. Four strategies (Harmony memory consideration, teaching-learning strategy, local pitch adjusting and random mutation) are employed to improve the performance of HS algorithm. Numerical results demonstrated very good computational performance.


0-1 optimization problem Operational research Harmony search Teaching-learning-based optimization 



This work is supported by Project of Youth Star in Science and Technology of Shaanxi Province (2016KJXX-95)


  1. 1.
    Dantzig, G.B., Fulkerson, D.R., Johnson, S.M.: Solution of a large-scale traveling salesman problem. Oper. Res. 2, 393–410 (1954)MathSciNetGoogle Scholar
  2. 2.
    Gomory, R.E.: Outline of an algorithm for integer solutions to linear programs. Bull. Am. Math. Soc. 64, 275–278 (1958)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Nemhauser, G.L., Wolsey, L.A.: Integer and Combinatorial Optimization. Wiley, New York (1988)CrossRefGoogle Scholar
  4. 4.
    Barnhart, C., Johnson, E.L., Nemhauser, G.L., et al.: Branch-and-price: column generation forsolving huge integer programs. Oper. Res. 48, 316–329 (1998)CrossRefGoogle Scholar
  5. 5.
    Wolsey, L.A.: Integer Programming. Wiley, New York (1998)zbMATHGoogle Scholar
  6. 6.
    Jiinger, M., Liebling, T., Naddef, D., et al.: 50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-Art. Springer, Berlin (2010)CrossRefGoogle Scholar
  7. 7.
    Li, D., Sun, X.L.: Nonlinear Integer Programming. Springer, New York (2006)zbMATHGoogle Scholar
  8. 8.
    Jourdan, L., Basseur, M., Talbi, E.G.: Hybridizing exact methods and metaheuristics: a taxonomy. Eur. J. Oper. Res. 199(3), 620–629 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zong, W.G., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simul. Trans. Soc. Model. Simul. Int. 76(2), 60–68 (2001)Google Scholar
  10. 10.
    Zhao, X., Liu, Z., Hao, J., et al.: Semi-self-adaptive harmony search algorithm. Nat. Comput. 16(4), 1–18 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yong, L., Liu, S., Zhang, J., Feng, Q.: Theoretical and empirical analyses of an improved harmony search algorithm based on differential mutation operator. J. Appl. Math. 2012, Article ID 147950Google Scholar
  12. 12.
    Tuo, S., Yong, L., Zhou, T.: An improved harmony search based on teaching-learning strategy for unconstrained optimization problems. Math. Probl. Eng. 2013, Article ID 413565, 29 pages. Scholar
  13. 13.
    Tuo, S., Yong, L., Deng, F.: A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems. Sci. World J. 2014, Article ID 637412, 19 pages. Scholar
  14. 14.
    Tuo, S., Zhang, J., Yong, L., Yuan, X.: A harmony search algorithm for high-dimensional multimodal optimization problems. Digit. Signal Process. Rev. J. 46(11), 151–163 (2015)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Tuo, S., Yong, L., et al.: HSTLBO: a hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems. PLoS ONE 12, e0175114 (2017)CrossRefGoogle Scholar
  16. 16.
    Wang, L., Hu, H., Liu, R., et al.: An improved differential harmony search algorithm for function optimization problems. Soft. Comput. 4, 1–26 (2018)Google Scholar
  17. 17.
    Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf. Sci. 183(1), 1–15 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Rao, R.V., Savsani, V.J., Balic, J.: Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng. Optim. 44(12), 1447–1462 (2012)CrossRefGoogle Scholar
  19. 19.
    Pardalos, P.M.: Construction of test problems in quadratic bivalent programming. ACM Trans. Math. Softw. 17(1), 74–87 (1991)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mathematics and Computer ScienceShaanxi University of TechnologyHanzhongChina

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