Advertisement

A Comparative Study of Techniques for Avoiding Premature Convergence in Harmony Search Algorithm

  • Krzysztof SzwarcEmail author
  • Urszula Boryczka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

The present article summarizes two techniques allowing to avoid premature convergence in Harmony Search algorithm, which was adapted for solving the instances of the Asymmetric Traveling Salesman Problem (ATSP). The efficiency of both approaches was demonstrated on the basis of the results of statistical test and ‘test bed’ consisting of nineteen instances of ATSP. The conclusion was that the best results were obtained in case of applying mechanisms which enable to reset the components of harmony memory at the moment of reaching stagnation. This process is controlled by parameters which are depended on the problem size.

Keywords

Harmony Search Asymmetric Traveling Salesman Problem Avoiding premature convergence 

References

  1. 1.
    Boryczka, U., Szwarc, K.: The adaptation of the harmony search algorithm to the ATSP. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10751, pp. 341–351. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75417-8_32CrossRefGoogle Scholar
  2. 2.
    Boryczka, U., Szwarc, K.: The adaptation of the harmony search algorithm to the ATSP with the evaluation of the influence of the pitch adjustment place on the quality of results. J. Inf. Telecommun. 3, 2–18 (2019)Google Scholar
  3. 3.
    Chen, S., Chen, R., Gao, J.: A modified harmony search algorithm for solving the dynamic vehicle routing problem with time windows. Sci. Program. 2017, 13 (2017)Google Scholar
  4. 4.
    Geem, Z.W.: Optimal design of water distribution networks using harmony search. Ph.D. thesis, Korea University (2000)Google Scholar
  5. 5.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: Harmony search optimization: application to pipe network design. Int. J. Model. Simul. 22(2), 125–133 (2002)CrossRefGoogle Scholar
  6. 6.
    Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Komaki, M., Sheikh, S., Teymourian, E.: A hybrid harmony search algorithm to minimize total weighted tardiness in the permutation flow shop. In: 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS), Orlando, FL, pp. 1–8 (2014)Google Scholar
  9. 9.
    Nowakowski, P., Szwarc, K., Boryczka, U.: Vehicle route planning in e-waste mobile collection on demand supported by artificial intelligence algorithms. Transp. Res. Part D: Transp. Environ. 63, 1–22 (2018)CrossRefGoogle Scholar
  10. 10.
    Osaba, E., Diaz, F., Onieva, E., Carballedo, R., Perallos, A.: A population metaheuristic with adaptive crossover probability and multi-crossover mechanism for solving combinatorial optimization problems. Int. J. Artif. Intell. 12, 1–23 (2014)Google Scholar
  11. 11.
    Öncan, T., Altınel, I.K., Laporte, G.: A comparative analysis of several asymmetric traveling salesman problem formulations. Comput. Oper. Res. 36(3), 637–654 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Rocha, M., Neves, J.: Preventing premature convergence to local optima in genetic algorithms via random offspring generation. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds.) IEA/AIE 1999. LNCS (LNAI), vol. 1611, pp. 127–136. Springer, Heidelberg (1999).  https://doi.org/10.1007/978-3-540-48765-4_16CrossRefGoogle Scholar
  13. 13.
    Syberfeldt, A., Rogstrom, J., Geertsen, A.: Simulation-based optimization of a real-world travelling salesman problem using an evolutionary algorithm with a repair function. Int. J. Artif. Intell. Expert Syst. (IJAE) 6(3), 27–39 (2015)Google Scholar
  14. 14.
    Zou, D., Gao, L., Li, S., Wu, J.: Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl. Soft Comput. 11(2), 1556–1564 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations