Apply Genetic Algorithm to Cloud Motion Wind

  • Jiang Han
  • Ling Li
  • Chengcheng Yang
  • Hui Tong
  • Longji Zeng
  • Tao Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)


Cloud Motion Wind (CMW) is a very important issue in the meteorology. In this paper, we firstly apply Genetic Algorithm (GA) to the CMW searching to reduce the computational complexity. We propose a novel CMW method, namely GA-CMW. Compared with the traditional Exhaustive CMW (E-CMW) algorithm, GA-CMW can obtain almost the same performance while with only 11 % of the computational complexity required. Generally speaking, the proposed GA-CMW method can obtain the wind vector picture in shorter time, which makes a lot of sense to the resource saving in the practical application.


Image matching Cloud motion wind Genetic algorithm Cross-correlation coefficient Computational complexity 



This paper is supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China under Grand 2012ZX03001039-002, which is kindly acknowledged.


  1. 1.
    Purdom JF (1996) W. Detailed cloud motions from satellite imagery taken at thirty second one and three minute intervals. In: Proceeding to the 3rd international wind workshop in Ascona, Switzerland, 10–12 June 1996, pp 137–146Google Scholar
  2. 2.
    Wang ZH, Browning KA, Kelly GA (1997) Verification of the tracking technique used in an experimental cloud motion wind inferring system. JCMM Report. University of Reading, 1997Google Scholar
  3. 3.
    Wang Z, Zhou J (2000) A preliminary study of Fourier series analysis for cloud tracking with GOES high temporal resolution images. Acta Meteoro Sin 14(1):82–94Google Scholar
  4. 4.
    Leese JA, Novak CS, Clark BB (1972) An automated technique for obtaining cloud motion from geosynchronous satellite data using cross correlation. J Appl Meteor 10(1):118–132CrossRefGoogle Scholar
  5. 5.
    Jianmin X, Qisong Z (1996) Calculation of cloud motion wind with GMS-5 images in China. In: Proceedings to the 3rd International Wind Workshop in Ascona Switzerland, pp 45–52, 10–12 June 1996Google Scholar
  6. 6.
    Revello TE, McCartney R (2002) Generating war game strategies using a genetic algorithm. In: Proceeding Congress Evolutionary Computation, 2002, vol. 2, pp 1086–1091Google Scholar
  7. 7.
    Campbell MS, Hoane AJ, Hus FH (2002) Deep blue. Artif Intell 134(1–2):57–83CrossRefMATHGoogle Scholar
  8. 8.
    Shibata T, Fukuda T, Tanie K (1997) Chapter 108: Synthesis of fuzzy, artificial intelligence, neural networks, and genetic algorithm for hierarchical intelligent control. CRC Press, Boca Raton, pp 1364–1368Google Scholar
  9. 9.
    Binelo MO, de Almeida ALF, Cavalcanti FRP (2011) MIMO array capacity optimization using a genetic algorithm. IEEE Trans Veh Technol 60(6):2471–2481CrossRefGoogle Scholar
  10. 10.
    Mangoud MAA (2009) Optimization of channel capacity for indoor MIMO systems using genetic algorithm. Prog Electromagn Res C 7:137–150Google Scholar
  11. 11.
    Bashir S, Khan AA, Naeem M, Shah SI (2007) An application of GA for symbol detection in MIMO communication systems. In: Third International Conference on Natural Computation, ICNC 2007, AugGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Jiang Han
    • 1
  • Ling Li
    • 2
  • Chengcheng Yang
    • 1
  • Hui Tong
    • 3
  • Longji Zeng
    • 4
  • Tao Yang
    • 4
  1. 1.Key Laboratory of Universal Wireless Communications, Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.College of Mathematic and InformationChina West Normal UniversitySichuanChina
  3. 3.School of ScienceBeijing University of Posts and TelecommunicationsBeijingChina
  4. 4.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina

Personalised recommendations