Abstract
As the development of human missions and space station, space rendezvous and docking technology is the key to modern space exploration. There are a lot of multi-objective optimization problems in aerospace field. At present, polymerization technology is often used to change multi-objective to single objective. This method makes the problem easier but gives one solution only which is not suitable for project application. In this paper, we introduce an extension of DE(SMODE) to cope with the spacecraft rendezvous problem. The experiment results indicate that SMODE is successful to locate the real Pareto front for the spacecraft rendezvous problem. Also, the effect of PopSize–population size and Max_gen–maximum number of generations of SMODE is studied.
Keywords
- Differential Evolution
- Multiobjective Optimization
- Nondominated Solution
- Trial Vector
- Rendezvous Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Jun, L.T.: Spacecraft Dynamics. Press of Harbin Institute of Technology (2003)
Wang, H., Tang, G.: Study on Appliaction of Genetic Algorithm in Spacecraft Optimal Rendezvous. Journal of Astronautics Control 1, 16–21 (2003)
Wang, S., Zhu, K.-j., Dai, J.-h., Ren, X.: Solving orbital transformation problems based on EA. Journal of Astronautics 23(1), 73–75 (2002)
Tang, Y., Chen, S., Xu, M., Wan, Z.: A genetic algorithm (GA) method of orbit interception with finite thrust. Journal of Northwestern Polytechnical University 23(5), 671–674 (2005)
Peng, L., Wu, Y., Hu, H.: Solving spacecraft rendezvous problems based on DE. In: The third Chinese astronautics institute deep-space committee academic conference, pp. 81–86 (2006)
Storn, R., Price, K.: Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Abbass, H.A.: The self-adaptive pareto differential evolution algorithm. In: CEC 2002. Congress on Evolutionary Computation, vol. 1, pp. 831–836. IEEE Service Center, Piscataway, New Jersey (2002)
Xue, F., Sanderson, A.C., Graves, R.J.: Pareto-based multi-objective differential evolution. In: CEC 2003. Proceedings of the 2003 Congress on Evolutionary Computation, Canberra, Australia, vol. 2, pp. 862–869. IEEE Press, NJ, New York (2003)
Robi, T., Filipic, B.: DEMO: Differential Evolution for Multi-objective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)
Babu, B.V., Jehan, M.M.L.: Differential Evolution for Multi-Objective Optimization. In: CEC 2003, Canberra, Australia, vol. 4, pp. 2696–2703 (December 2003)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGACII. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Angira, R., Babu, B.V.: Non-dominated sorting differential evolution (NSDE):an extension of differential evolution for multi-objective optimization. In: IICAI-05. 2nd Indian international conference artificial intellgence, pp. 1428–1443 (2005)
Madavan, N.K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 1145–1150. IEEE Computer Society Press, Los Alamitos (2002)
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Peng, L., Dai, G., Chen, F., Liu, F. (2007). Study on Application of Multi-Objective Differential Evolution Algorithm in Space Rendezvous. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_5
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DOI: https://doi.org/10.1007/978-3-540-74581-5_5
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