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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6, 154–160 (1994)
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181, 1653–1669 (2007)
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)
Cordeau, J.F., Laporte, G.: Maximizing the value of an earth observation satellite orbit. J. Oper. Res. Soc. 56, 962–968 (2005)
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)
Gonçalves, J.F., Almeida, J.: A hybrid genetic algorithm for assembly line balancing. J. Heuristics 8, 629–642 (2002)
Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics 17, 487–525 (2011)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Tangpattanakul, P., Jozefowiez, N., Lopez, P. (2015). Biased Random Key Genetic Algorithm for Multi-user Earth Observation Scheduling. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 580. Springer, Cham. https://doi.org/10.1007/978-3-319-12631-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-12631-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12630-2
Online ISBN: 978-3-319-12631-9
eBook Packages: EngineeringEngineering (R0)