Abstract
Regional coverage Constellation Optimizing Design is a classical dynamic multi-objective optimizing problem. Against low efficiency of traditional multi-objective evolutionary algorithms and poor utilization of Pareto-optimal solutions distribution regularity etc, in this papera new approach OMEA which bases on the probability-model utilizing Pareto-optimal solutions distribution regularity to obtain a good distribution of Pareto-optimal solutions, we also apply the quantization technique and orthogonal design to generate initial points which spread uniformly in the feasible solution space. Considering coverage rate assessment criterions, we accomplish the design and simulation of Leo Constellation. Compared with NSGA-II, Pareto solutions by OMEA are closer to Pareto-optimal Front. The result of experiments shows a group of Pareto solutions with a uniform distribution can be achieved, which gives strong supports to constellation design determination.
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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)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (2001)
Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Ph. D. thesis, Vanderbilt University (unpublished, 1984)
Horn, J., Nafpliotis, N.: Multiobjective optimization using the niched pareto genetic algorithm. IlliGAL Report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Champaign (1993)
Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)
Jin, Y., Sendhoff, B.: Connectedness, regularity and the success of local search in evolutionary multi-objective optimization. In: Congress on Evolutionary Computation, Canberra, Australia, pp. 1910–1917. IEEE Computer Society Press, Los Alamitos (2003)
Zhou, A., Zhang, Q., Jin, Y., Tsang, E., Okabe, T.: A model-based evolutionary algorithm for bi-objective optimization. In: Congress on Evolutionary Computation, Edinburgh, UK, IEEE Computer Society Press, Los Alamitos (2005)
Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Combining Model-based and Genetics-based Offspring Generation for Multi-objective Optimization Using a Convergence Criterion[C].cec2006 (2006)
Zeng, S., Kang, L., Ding, L.: An Orthogonal Multiobjective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints. Evolutionary Computation 12(1), 77–98 (2004)
Cai, Z., Gong, W., Huang, Y.: A Novel Differential Evolution Algorithm based on -domination and Orthogonal Design Method for Multiobjective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 286–301. Springer-Verlag, Heidelberg (2007)
Larranaga, P., Lozano, J.A (eds.): Estimation of DistributionAlgorithms. Kluwer Academic Publishers, Dordrecht (2001)
Kambhatla, N., Leen, T.K.: Dimension reduction by local principal component analysis. Neural Computation 9(7), 1493–1516 (1997)
Smith, L.I.: A tutorial on Principal Components Analysis [EB/OL] (2000), http://www.cs.otago.ac.nz/cosc453/studenttutorials/principalcomponents.pdf
Leung, Y.W., Wang, Y.: An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation 5(1), 41–53 (2001)
Li, S., Zhujiang, Li, G.: Multi-object optimization of LEO regional communication satellite constellation with GA algorithm. Journal of PLA University of Science and Technology 6(1), 1–6 (2005)
Frayss Inhes, E.: Investigating new satellite constellat ion geometries with genetic algorithm [A]. In: AIAA 9623636, AAS/AIAA A strodynamics Specialist Conference [C], San Diego, American Institute of Aeronautics and Astronautics Inc. (1996)
Mason, W.J., Victoriav,: Optimal earth orbiting satellite constellation via a pareto genetic algorithm[A]. In: AAS002139, AAS/AIAA A strodynamics Specialist Conference and Exhibit [C], San Diego, American Institute of Aeronautics and Astronautics Inc. (1998)
Ferringer, M.P., Spencer, D.B.: Satellite Constellation Design Tradeoffs Using Multiple-Objective Evolutionary Computation. Journal of Spacecraft and Rockets 43(6) (November-December 2006)
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Dai, G., Zheng, W., Xie, B. (2007). An Orthogonal and Model Based Multiobjective Genetic Algorithm for LEO Regional Satellite Constellation Optimization. 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_71
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DOI: https://doi.org/10.1007/978-3-540-74581-5_71
Publisher Name: Springer, Berlin, Heidelberg
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