• DocumentCode
    478614
  • Title

    Hybrid Algorithms for Electromagnetic Detection Satellites Scheduling

  • Author

    Chen, Hao ; Li, Jun ; Jing, Ning ; Tang, Yu

  • Author_Institution
    Dept. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol. Changsha, Changsha
  • Volume
    1
  • fYear
    2008
  • fDate
    3-5 Nov. 2008
  • Firstpage
    411
  • Lastpage
    418
  • Abstract
    Electromagnetic detection satellite (EDS) is a type of Earth observation Satellites (EOSs). The Information collected by EDSs is very important in some application domain, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, little work on the scheduling of EDSs for its specific requirements. Considering the specific constrains of EDSs, we established a MultiSatellites scheduling model and proposed a scheduling algorithm based on genetic algorithm. A hybrid algorithm incorporated with genetic algorithm and stochastic climbing algorithm was constructed to improve the scheduling algorithm. To deal with some specific constrains, a punish function method was introduced. We have conducted some experiments to validate correctness and practicability of our scheduling algorithms.
  • Keywords
    artificial satellites; combinatorial mathematics; optimisation; remote sensing; scheduling; Earth observation satellites; combinatorial optimization problem; electromagnetic detection satellites scheduling; hybrid algorithms; multi-satellites scheduling; Artificial satellites; Defense industry; Genetic algorithms; Greedy algorithms; Job shop scheduling; Military satellites; Scheduling algorithm; Sensor phenomena and characterization; Stochastic processes; Synthetic aperture radar; Electromagnetic Detection Satellites; Genetic Algorithm; Hybrid Algorithm; Punish Function Method; Stochastic Climbing Algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2008. ICTAI '08. 20th IEEE International Conference on
  • Conference_Location
    Dayton, OH
  • ISSN
    1082-3409
  • Print_ISBN
    978-0-7695-3440-4
  • Type

    conf

  • DOI
    10.1109/ICTAI.2008.8
  • Filename
    4669718