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Adaptively Calling Selection Based on Distance Sorting in CoBiDE

  • Zhe ChenEmail author
  • Chengjun Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 986)

Abstract

Differential Evolution is fit for solving continuous optimization problems. So far, the imbalance between exploration and exploitation in DE runs often leads to the failure to obtain good solutions. In this paper, we propose selection based on distance sorting. In such selection, the individual has the best fitness among parents and offspring is selected firstly. Then, the genotype distance from another individual to it, the distance in their chromosome structure, decides whether the former individual is selected. Under the control of a adaptive scheme proposed by us, we use it replace the original selection of the CoBiDE in runs from time to time. Experimental results show that, for many among the twenty-five CEC 2005 benchmark functions, which have the similar changing trend of diversity and fitness in runs, our adaptive scheme for calling selection based on distance sorting brings improvement on solutions.

Keywords

Exploration and exploitation balance Secondary selection Stagnation Premature convergence CoBiDE 

References

  1. 1.
    Ali, M.Z., Awad, N.H., Suganthan, P.N., Reynolds, R.G.: An adaptive multipopulation differential evolution with dynamic population reduction. IEEE Trans. Cybern. 47(9), 2768–2779 (2017)CrossRefGoogle Scholar
  2. 2.
    Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: An ensemble sinusoidal parameter adaptation incorporated with L-shade for solving CEC2014 benchmark problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2958–2965. IEEE (2016)Google Scholar
  3. 3.
    Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: CADE: a hybridization of cultural algorithm and differential evolution for numerical optimization. Inf. Sci. 378, 215–241 (2017)CrossRefGoogle Scholar
  4. 4.
    Črepinšek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)CrossRefGoogle Scholar
  5. 5.
    Du, W., Leung, S.Y.S., Tang, Y., Vasilakos, A.V.: Differential evolution with event-triggered impulsive control. IEEE Trans. Cybern. 47(1), 244–257 (2017)CrossRefGoogle Scholar
  6. 6.
    Fan, Q., Yan, X.: Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Trans. Cybern. 46(1), 219–232 (2016)CrossRefGoogle Scholar
  7. 7.
    Fu, C., Jiang, C., Chen, G., Liu, Q.: An adaptive differential evolution algorithm with an aging leader and challengers mechanism. Appl. Soft Comput. 57, 60–73 (2017)CrossRefGoogle Scholar
  8. 8.
    Guo, Z., Liu, G., Li, D., Wang, S.: Self-adaptive differential evolution with global neighborhood search. Soft Comput. 21(13), 3759–3768 (2017)CrossRefGoogle Scholar
  9. 9.
    Islam, S.M., Das, S., Ghosh, S., Roy, S., Suganthan, P.N.: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 482–500 (2012)CrossRefGoogle Scholar
  10. 10.
    Jadon, S.S., Tiwari, R., Sharma, H., Bansal, J.C.: Hybrid artificial bee colony algorithm with differential evolution. Appl. Soft Comput. 58, 11–24 (2017)CrossRefGoogle Scholar
  11. 11.
    Li, G., et al.: A novel hybrid differential evolution algorithm with modified CoDE and JADE. Appl. Soft Comput. 47, 577–599 (2016)CrossRefGoogle Scholar
  12. 12.
    Liao, J., Cai, Y., Wang, T., Tian, H., Chen, Y.: Cellular direction information based differential evolution for numerical optimization: an empirical study. Soft Comput. 20(7), 2801–2827 (2016)CrossRefGoogle Scholar
  13. 13.
    Mohamed, A.W., Suganthan, P.N.: Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput. 1–21 (2017)Google Scholar
  14. 14.
    Qiu, X., Tan, K.C., Xu, J.X.: Multiple exponential recombination for differential evolution. IEEE Trans. Cybern. 47(4), 995–1006 (2017)CrossRefGoogle Scholar
  15. 15.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Tatsis, V.A., Parsopoulos, K.E.: Differential evolution with grid-based parameter adaptation. Soft Comput. 21(8), 2105–2127 (2017)CrossRefGoogle Scholar
  17. 17.
    Tian, M., Gao, X., Dai, C.: Differential evolution with improved individual-based parameter setting and selection strategy. Appl. Soft Comput. 56, 286–297 (2017)CrossRefGoogle Scholar
  18. 18.
    Wang, Y., Li, H.X., Huang, T., Li, L.: Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl. Soft Comput. 18, 232–247 (2014)CrossRefGoogle Scholar
  19. 19.
    Wu, G., Mallipeddi, R., Suganthan, P.N., Wang, R., Chen, H.: Differential evolution with multi-population based ensemble of mutation strategies. Inf. Sci. 329, 329–345 (2016)CrossRefGoogle Scholar
  20. 20.
    Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Suganthan, P.: Ensemble of differential evolution variants. Inf. Sci. 423, 172–186 (2018)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yi, W., Zhou, Y., Gao, L., Li, X., Mou, J.: An improved adaptive differential evolution algorithm for continuous optimization. Expert Syst. Appl. 44, 1–12 (2016)CrossRefGoogle Scholar
  22. 22.
    Zheng, L.M., Liu, L., Zhang, S.X., Zheng, S.Y.: Enhancing differential evolution with interactive information. Soft Comput. 1–20 (2017)Google Scholar
  23. 23.
    Zheng, L.M., Zhang, S.X., Tang, K.S., Zheng, S.Y.: Differential evolution powered by collective information. Inf. Sci. 399, 13–29 (2017)CrossRefGoogle Scholar
  24. 24.
    Zhou, Y.Z., Yi, W.C., Gao, L., Li, X.Y.: Adaptive differential evolution with sorting crossover rate for continuous optimization problems. IEEE Trans. Cybern. 47(9), 2742–2753 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Intelligent Geo-Information ProcessingChina University of GeosciencesWuhanChina

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