Multimedia Tools and Applications

, Volume 76, Issue 13, pp 14951–14968 | Cite as

An improved cultural algorithm and its application in image matching

  • Xuesong Yan
  • Tao Song
  • Qinghua Wu


Cultural Algorithm (CA) are a class of computational models derived from observing the cultural evolution process in nature and is used to solve complex calculations of the new global optimization search algorithms. Aiming at the traditional cultural algorithm has poor precision and trap into local optimum of global optimization. In this paper, introduce the isolation niche technology into the traditional cultural algorithm. With improvements, the algorithm is less likely to trap in local optimum. According to the test of one set of benchmark function, the proposed algorithm has greater improvements than ordinal cultural algorithm in the aspects of convergence precision and stability. In this paper, introduce the proposed algorithm into the image matching problem, and the simulation test shows that the algorithm for image matching problem has made great effects in stability and convergence precision.


Cultural algorithm Isolation niche technology Population Image matching 



This paper is supported by National Natural Science Foundation of China (No. 41404076, 61402425, 61501412, 61673354, 61672474).


  1. 1.
    Ali MZ et al (2016) A modified cultural algorithm with a balanced performance for the differential evolution frameworks. Knowl-Based Syst 111:73–86CrossRefGoogle Scholar
  2. 2.
    Bellman R (1956) Dynamic programming and Lagrange multipliers. Proc Natl Acad Sci 42(10):767–769Google Scholar
  3. 3.
    Chung C (1997) Knowledge-based approaches to self-adaptation in cultural algorithms. Ph. D. Thesis, Wayne State University, Detroit, Michigan, USAGoogle Scholar
  4. 4.
    Goldbeg DE (1989) Genetic algorithms in search, optimization and machine learning, reading. Addison-Wesley, MassGoogle Scholar
  5. 5.
    Gong W, Cai Z, Ling CX, Li H (2011) Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41(2):397–413CrossRefGoogle Scholar
  6. 6.
    Gong W, Cai Z, Wang Y (2014) Repairing the crossover rate in adaptive differential evolution. Appl Soft Comput 15:149–168CrossRefGoogle Scholar
  7. 7.
    Gong W, Zhou A, Cai Z (2015a) A multioperator search strategy based on cheap surrogate models for evolutionary optimization. IEEE Trans Evol Comput 19(5):746–758CrossRefGoogle Scholar
  8. 8.
    Gong W, Cai Z, Liang D (2015b) Adaptive ranking mutation operator based differential evolution for constrained optimization. IEEE Transactions on Cybernetics 45(4):s on Electrical 716–s on Electrical 727CrossRefGoogle Scholar
  9. 9.
    Gong W, Yan X, Liu X, Cai Z (2015c) Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy 86:139–151CrossRefGoogle Scholar
  10. 10.
    Haldar V, Chakraborty N (2015) Power loss minimization by optimal capacitor placement in radial distribution system using modified cultural algorithm. Int Trans Electr Energy Syst 25(1):54–71CrossRefGoogle Scholar
  11. 11.
    Hong C et al (2016) Realtime and robust object matching with a large number of templates. Multimed Tools Appl 75(3):1459–1480CrossRefGoogle Scholar
  12. 12.
    Hu C, Zhao J, Yan X, Zeng D, Guo S (2015) A mapreduce based parallel niche genetic algorithm for contaminant source identi_cation in water distribution network. Ad Hoc Netw 35(C):116–126CrossRefGoogle Scholar
  13. 13.
    Jarraya SK, Hammami M, Ben-Abdallah H (2015) Adaptive moving shadow detection and removal by new semi-supervised learning technique. Multimed Tools Appl 75(18):10949–10977CrossRefGoogle Scholar
  14. 14.
    Jia X et al (2016) A novel edge detection approach using a fusion model. Multimed Tools Appl 75(2):1099–1133CrossRefGoogle Scholar
  15. 15.
    Li C, Nguyen TT, Yang M, Yang S, Zeng S (2015) Multi-population methods in unconstrained continuous dynamic environments: the challenges. Inf Sci 296:95–118CrossRefGoogle Scholar
  16. 16.
    Lin Z, Yan J, Yuan Y (2016) Target detection for SAR images based on beamlet transform. Multimed Tools Appl 75(4):2189–2202CrossRefGoogle Scholar
  17. 17.
    Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd edn. Springer-Verlag, BerlinCrossRefzbMATHGoogle Scholar
  18. 18.
    Nam Y (2016) Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes. Multimed Tools Appl 75(12):7003–7028CrossRefGoogle Scholar
  19. 19.
    Nian F et al (2016) Efficient near-duplicate image detection with a local-based binary representation. Multimed Tools Appl 75(5):2435–2452CrossRefGoogle Scholar
  20. 20.
    Reynoids R (1994) An introduction to cultural algorithms. Proceedings of the 3rd Annual Conference on Evolutionary Programming, 131–139Google Scholar
  21. 21.
    Sim DG, Kwon OK, Park RH (1999) Object matching algorithms using robust Hausdorff distance measures. IEEE Trans Image Process 8(3):425–429CrossRefGoogle Scholar
  22. 22.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  23. 23.
    Wu Q, Zhang J, Huang W, Sun Y (2014) An efficient image matching algorithm based on culture evolution. J Chem Pharm Res 6(5):271–278Google Scholar
  24. 24.
    Xie S, Wang Y (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(1):231–246CrossRefGoogle Scholar
  25. 25.
    Yan X, Wu Q, Sheng VS (2016) A double weighted naive Bayes with niching cultural algorithm for multi-label classification. Int J Pattern Recognit Artif Intell 30:1650013. doi: 10.1142/S0218001416500130
  26. 26.
    Yan X, Hu C, Yao H et al (2015) Adaptive cultural algorithm for combinational digital circuit sensor design. Sens Lett 13(2):127–129CrossRefGoogle Scholar
  27. 27.
    Yan XS, Wu QH (2012) Function optimization based on cultural algorithms. J Comput Inf Technol 2:152–158Google Scholar
  28. 28.
    Yan L, Ju H, Zhuoshang J, Yinsheng D (2000) Isolation of niching genetic algorithms research. Syst Eng J 15(1):86–91Google Scholar
  29. 29.
    Yan X, Wu Q, Zhang C et al (2012) An efficient function optimization algorithm based on culture evolution. Int J Comput Sci Issues 9:11–18Google Scholar
  30. 30.
    Yan X, Hu C, Yao H et al (2013) Circuit optimization design based on improved cultural algorithm. Int J Adv Comput Technol 5:122–130Google Scholar
  31. 31.
    Yan X, Wu Q, Liu H (2015) Digital circuit optimization design algorithm based on cultural evolution. Metall Min Ind 7(9):877–885Google Scholar
  32. 32.
    Yan X, Zhao J, Hu C, Wu Q (2016) Contaminant source identification in water distribution network based on hybrid encoding. J Comput Methods Sci Eng 16(2):379–390CrossRefGoogle Scholar
  33. 33.
    Yang M, Li C, Cai Z, Guan J (2015) Differential evolution with auto-enhanced population diversity. IEEE Transactions Cybern 45(2):302–315CrossRefGoogle Scholar
  34. 34.
    Zadeh PM, Kobti Z (2015) A multi-population cultural algorithm for community detection in social networks. Procedia Comput Sci 52:342–349CrossRefGoogle Scholar
  35. 35.
    Zadeh PM, Pandey M, Kobti Z (2016) A study on population adaptation in social networks based on knowledge migration in cultural algorithm. 2016 I.E. Congress on IEEE Evolutionary Computation (CEC), 4405–4412Google Scholar
  36. 36.
    Zhang Y (2008) Cultural algorithm and its application in the portfolio. Master Thesis, Harbin University of Science and Technology, Harbin, ChinaGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Center of Network and Educational TechnologyChin University of GeosciencesWuhanChina
  3. 3.Faculty of Computer Science and EngineeringWuHan Institute of TechnologyWuhanChina

Personalised recommendations