Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling

  • Panwadee Tangpattanakul
  • Nicolas Jozefowiez
  • Pierre Lopez
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 580)

Abstract

This paper presents a biased random key genetic algorithm, or BRKGA, for solving a multi-user observation scheduling problem. BRKGA is an efficient method in the area of combinatorial optimization. It is usually applied to single objective problem. It needs to be adapted for multi-objective optimization. This paper considers two adaptations. The first one presents how to select the elite set, i.e., good solutions in the population. We borrow the elite selection methods from efficient multi-objective evolutionary algorithms. For the second adaptation, since the multi-objective optimization needs a set of solutions on the Pareto front, we investigate the idea to obtain several solutions from a single chromosome. Experiments are conducted on realistic instances, which concern the multi-user observation scheduling of an agile Earth observing satellite.

Keywords

Multi-objective optimization Scheduling Earth observing satellite Genetic algorithm 

References

  1. 1.
    Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6, 154–160 (1994)CrossRefMATHGoogle Scholar
  2. 2.
    Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181, 1653–1669 (2007)CrossRefMATHGoogle Scholar
  3. 3.
    Bianchessi, N., Cordeau, J.F., Desrosiers, J., Laporte, G., Raymond, V.: A heuristic for the multi-satellite, multi-orbit and multi-user management of earth observation satellites. Eur. J. Oper. Res. 177, 750–762 (2007)CrossRefMATHGoogle Scholar
  4. 4.
    Cordeau, J.F., Laporte, G.: Maximizing the value of an earth observation satellite orbit. J. Oper. Res. Soc. 56, 962–968 (2005)CrossRefMATHGoogle Scholar
  5. 5.
    Deb, K., Pratep, A., Agarwal, S., Meyarivan, T.: A fast and elite multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Gonçalves, J.F., Almeida, J.: A hybrid genetic algorithm for assembly line balancing. J. Heuristics 8, 629–642 (2002)CrossRefGoogle Scholar
  7. 7.
    Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics 17, 487–525 (2011)CrossRefGoogle Scholar
  8. 8.
    Goulart, N., de Souza, S. R., Dias, L. G. S., Noronha, T. F.: Biased Random-key Genetic Algorithm for Fiber Installation in Optical Network Optimization. In: IEEE Congress on Evolutionary Computation, pp. 2267–2271. New Orleans (2011)Google Scholar
  9. 9.
    Knowles, J., Thiele, L., Zitzler, E.: Technical report, Computer Engineering and Networks Laboratory (TIK). A tutorial on the performance assessment of stochastic multiobjective optimizers. ETH Zurich, Switzerland (2006)Google Scholar
  10. 10.
    Kuipers, E. J.: An Algorithm for Selecting and Timetabling Requests for an Earth Observation Satellite. Bulletin de la Société Française de Recherche Opérationnelle et d’Aide à la Décision, pp. 7–10 (2003) (available at: http://www.roadef.org/content/roadef/bulletins/bulletinNo11.pdf)
  11. 11.
    Mendes, J.J.M., Gonçalves, J.F., Resende, M.G.C.: A random key based genetic algorithm for the resource constrained project scheduling problem. Comput. Oper. Res. 36, 92–109 (2009)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Tangpattanakul, P., Jozefowiez, N., Lopez, P.: Multi-objective Optimization for Selecting and Scheduling Observations by Agile Earth Observing Satellites. In: Coello Coello, C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN XII. LNCS, vol. 7492, pp. 112–121. Springer, Heidelberg (2012)Google Scholar
  13. 13.
    Verfaillie, G., Lemaître, M., Bataille, N., Lachiver, J. M.: Management of the mission of earth observation satellites challenge description. Technical report, Centre National d’Etudes Spatiales, France (2002) (available at: http://challenge.roadef.org/2003/files/formal_250902.pdf)
  14. 14.
    Zitzler, E., Künzli, S.: Indicator-Based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Panwadee Tangpattanakul
    • 1
  • Nicolas Jozefowiez
    • 2
    • 3
  • Pierre Lopez
    • 2
    • 4
  1. 1.Geo-Informatics and Space Technology Development Agency (GISTDA)BangkokThailand
  2. 2.CNRSLAASToulouseFrance
  3. 3.INSA, LAASUniv de ToulouseToulouseFrance
  4. 4.LAASUniv de ToulouseToulouseFrance

Personalised recommendations