Advertisement

Applied Intelligence

, Volume 49, Issue 2, pp 605–627 | Cite as

A differential evolution algorithm with dual preferred learning mutation

  • Meijun DuanEmail author
  • Hongyu Yang
  • Hong Liu
  • Junyi Chen
Article
  • 103 Downloads

Abstract

Differential evolution (DE) algorithm is widely used for solving real-parameter optimization problems due to its simplicity and efficiency. However the canonical DE is easy to suffer from the premature convergence. To further improve the performance of the DE, a differential evolution algorithm with dual preferred learning mutation (DPLDE) is proposed. Dual preferred learning mutation simultaneously learns behaviors from the individual with better fitness(BFI) and individual with better diversity(BDI). The learning factor of BFI is self-adaptively and independently adjusted for each individual. The learning factor of BDI is adaptively adjusted at each generation. A total of 26 Benchmark test functions with different characteristics are used for performance comparative experiments. The results show that DPLDE is superior to the eight state-of-the-art improved algorithms in terms of the convergence precision, convergence speed and stability. For the high-dimensional functions, with the same-scale population and maximum number of evolution generations, DPLDE can still get the excellent global optimization performance and has a more prominent advantage.

Keywords

Differential evolution Global optimization Dual preferred learning mutation Learning factor 

Notes

Funding Information

This work is supported by the National 863 Project (NO. 2015AA016405).

References

  1. 1.
    Storn R, Price K (1995) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. University of California, BerkeleyzbMATHGoogle Scholar
  2. 2.
    Zheng JG, Wang X (2011) Diversity composite differential evolution algorithm for constrained optimization problems. Comput Integr Manuf Syst 17(11):2447–2456Google Scholar
  3. 3.
    Wang WL, Wang L, Wang HY et al (2012) Dynamic Job Shop scheduling based on hybrid differential evolution algorithm. Comput Integr Manuf Syst 18(3):531–539Google Scholar
  4. 4.
    Ghosh A, Datta A, Ghosh S (2013) Self-adaptive differential evolution for feature selection in hyperspectral image data. Appl Soft Comput 13(4):1969–1977Google Scholar
  5. 5.
    Nyirarugira C, Kim T (2013) Adaptive differential evolution algorithm for real time object tracking. IEEE Trans Consum Electron 59(4):833–838Google Scholar
  6. 6.
    Marcic T, Stumberger B, Stumberger G (2014) Differential evolution based parameter identification of a line-start IPM synchronous motor. IEEE Trans Indust Electron 61(11):5921–5929Google Scholar
  7. 7.
    Kadhar KMA, Baskar S, Amali SMJ (2015) Diversity controlled self-adaptive differential evolution based design of non-fragile multivariable PI controller. Eng Appl Artif Intell 46:209–222Google Scholar
  8. 8.
    Zhang J, Sanderson AC (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958Google Scholar
  9. 9.
    Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaption for global numerical optimization. IEEE Trans Evol Comput 13:398–417Google Scholar
  10. 10.
    Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66Google Scholar
  11. 11.
    Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696Google Scholar
  12. 12.
    Elsayed SM, Sarker RA, Essam DL (2014) A self-adaptive combined strategies algorithm for constrained optimization using differential evolution. Appl Math Comput 241:267–282MathSciNetzbMATHGoogle Scholar
  13. 13.
    Gou J, Guo W -P, Hou F, Wang C, Cai Y -Q (2015) Adaptive differential evolution with directional strategy and cloud model. Appl Intell 42:369–388Google Scholar
  14. 14.
    Yi W, Gao L, Li X, Zhou Y (2015) A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems. Appl Intell 42:642–660Google Scholar
  15. 15.
    Tang L, Dong Y, Liu J (2015) Differential evolution with an individual-dependent mechanism. IEEE Trans Evol Comput 19(4):560–574Google Scholar
  16. 16.
    Guo S-M, Yang C-C, Hsu P-H, Tsai JS-H (2015) Improving differential evolution with a successful-parent-selecting framework. IEEE Trans Evol Comput 19(5):717–730Google Scholar
  17. 17.
    Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345Google Scholar
  18. 18.
    Yeh M-F, Lu H-C, Chen T-H, Leu M-S (2017) Modified Gaussian barebones differential evolution with hybrid crossover strategy. In: Proceedings of the 2016 international conference on machine learning and cybernetics, pp 7–12Google Scholar
  19. 19.
    Cai Y, Sun G, Wang T, Tian H, Chen Y, Wang J (2017) Neighborhood-adaptive differential evolution for global numerical optimization. Appl Soft Comput 59:659–706Google Scholar
  20. 20.
    Tang R (2017) Decentralizing and coevolving differential evolution for large-scale global optimization problems. Appl Intell 47:1208–1223Google Scholar
  21. 21.
    Wang S, Li Y, Yang H (2017) Self-adaptive differential evolution algorithm with improved mutation mode. Appl Intell 47:644–658Google Scholar
  22. 22.
    Nenavath H, Jatoth RK (2018) Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Appl Soft Comput 62:1019–1043Google Scholar
  23. 23.
    He X, Zhou Y (2018) Enhancing the performance of differential evolution with covariance matrix self-adaptation. Appl Soft Comput 64:227–243Google Scholar
  24. 24.
    Zhang X, Kang Q, Cheng J, Wang X (2018) A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer. Appl Soft Comput 67:197–214Google Scholar
  25. 25.
    Cui L, Li G, Zhu Z, Lin Q, Wong K -C, Chen J, Lu N, Lu J (2018) Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism. Inf Sci 422:122–142MathSciNetGoogle Scholar
  26. 26.
    Wu G, Shen X, Li H, Chen H, Lin A, Suganthan PN (2018) Ensemble of differential evolution variants. Inf Sci 423:172– 186MathSciNetGoogle Scholar
  27. 27.
    Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462zbMATHGoogle Scholar
  28. 28.
    Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical Benchmark problems. IEEE Trans Evol Comput 10 (6):646–657Google Scholar
  29. 29.
    Nasimul N, Danushka B, Hitoshi I (2011) An adaptive differential evolution algorithm. In: IEEE congress on evolutionary computation. IEEE Press, New Orleans, pp 2229–2236Google Scholar
  30. 30.
    Gong W, Cai Z (2013) Differential evolution with ranking-based mutation operators. IEEE Trans Cybern 43(6):2066–2081Google Scholar
  31. 31.
    Zhu W, Tang Y, Fang J -A, Zhang W (2013) Adaptive population tuning scheme for differential evolution. Inf Sci 223:164–191Google Scholar
  32. 32.
    Tanabe R, Fukunaga A (2013) Success-history based parameter adaptation for differential evolution. In: IEEE congress on evolutionary computation, pp 71–78Google Scholar
  33. 33.
    Wenyin G, Zhihua C, Yang W (2014) Repairing the crossover rate in adaptive differential evolution. Appl Soft Comput 15:149–168Google Scholar
  34. 34.
    Fan Q, Yan X (2015) Self-adaptive differential evolution algorithm with discrete mutation control parameters. Expert Syst Appl 42: 1551–1572Google Scholar
  35. 35.
    Wang Y, Cai Z, Zhang Q (2012) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 185:153–177MathSciNetGoogle Scholar
  36. 36.
    Xie Y, Zhao C-X, Zhang H-F, Yan X-J, Chen D-B (2015) A blending crossover differential evolution approach to camera space manipulation parameter optimization. Acta Phys Sin 2:1–7Google Scholar
  37. 37.
    Cai Y, Wang J (2015) Differential evolution with hybrid linkage crossover. Inf Sci 320:244–287MathSciNetGoogle Scholar
  38. 38.
    Guo S -M, Yang C -C (2015) Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans Evol Comput 19(1):31–49MathSciNetGoogle Scholar
  39. 39.
    Xu Y, Fang J-A, Zhu W, Wang X, Zhao L (2015) Differential evolution using a superior-inferior crossover scheme. Comput Optim Appl 61:243–274MathSciNetzbMATHGoogle Scholar
  40. 40.
    Ghosh A, Das S, Mullick SS, Mallipeddi R, Das AK (2017) A switched parameter differential evolution with optional blending crossover for scalable numerical optimization. Appl Soft Comput 57:329–352Google Scholar
  41. 41.
    Cheng R, Jin Y (2015) A social learning particle swarm optimization algorithm for scalable optimization. Inf Sci 291:43–60MathSciNetzbMATHGoogle Scholar
  42. 42.
    Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, BerlinzbMATHGoogle Scholar
  43. 43.
    Storn R, Price K (2010) Home page of differential evolution. Int Comput Sci Inst, Berkeley, CA, USAGoogle Scholar
  44. 44.
    Montgomery J, Chen S (2010) An analysis of the operation of differential evolution at high and low crossover rates. In: 2010 IEEE congress on evolutionary computation, (CEC), pp 1–8Google Scholar
  45. 45.
    Liang JJ, Qu BY, Suganthan PN, Chen Q (2015) Problem definition and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimizationGoogle Scholar
  46. 46.
    Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, pp 1–50Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Meijun Duan
    • 1
    Email author
  • Hongyu Yang
    • 1
    • 2
  • Hong Liu
    • 2
  • Junyi Chen
    • 1
  1. 1.National Key Laboratory of Fundamental Science on Synthetic VisionSichuan UniversityChengduChina
  2. 2.National Key Laboratory of Air Traffic Control Automation System TechnologySichuan UniversityChengduChina

Personalised recommendations