Abstract
A Pareto genetic algorithm is applied to the optimization of low-thrust interplanetary spacecraft trajectories. A multiobjective, nondominated sorting genetic algorithm is developed following existing methodologies. A hybridized scheme is designed integrating the nondominated sorting genetic algorithm with a calculus-of-variations-based trajectory optimization algorithm. “Families” of Pareto optimal trajectories are generated for the cases of Earth-Mars flyby and rendezvous trajectories. A novel trajectory type generated by the genetic algorithm is expanded to develop a series of versatile, high-performance Earth-Mars rendezvous trajectories.
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SRINIVAS, N., and DEB, K. “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,” Evolutionary Computation, Vol. 2, No. 3, 1995, pp. 221–248.
RAUWOLF, G., and COVERSTONE-CARROLL, V. “Near-optimal Low-thrust Orbit Transfers Generated by a Genetic Algorithm,” Journal of Spacecraft and Rockets, Vol. 33, No. 6, 1996, pp. 859–862.
HANS, A. E. “Multicriteria Optimization for Highly Accurate Systems,” Multicriteria Optimization in Engineering and Sciences, edited by W. Stadler, Vol. 19, Mathematical Concepts and Methods in Science and Engineering, Plenum Press, New York, 1988, pp. 309–352.
RAO, S. S. Optimization Theory and Application, Wiley Eastern Limited, New Delhi, 1991.
GOLDBERG, D. E. Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.
DEAN, E. B. “Multi-objective Optimization from the Perspective of Competitive Advantage” [online], NASA Langley Research Center, http://mijuno.larc.nasa.gov/dfc/mdo/moo.html/dfc/mdo/moo.html [cited February 3, 1999].
FONSECA, C. M., and FLEMING, P. J. “Genetic Algorithms for Multi-objective Optimization: Formula, Discussion, and Generalization,” Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, California, 1993, pp. 416–423.
SCHAFFER, J. D. “Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms,” Ph.D. Dissertation, Vanderbilt University, Nashville, Tennessee, May 1984.
HARTMANN, J. W. “Low-thrust Trajectory Optimization Using Stochastic Optimization Methods,” M. S. Thesis, Department of Aeronautical and Astronautical Engineering, University of Illinois at Urbana-Champaign, Illinois, January 1999.
SAUER, C. G. “Optimization of Multiple Target Electric Propulsion Trajectories,” AIAA Paper 73–205, AIAA 11th Aerospace Sciences Meeting, Washington, D. C., January 1973.
MOSCATO, P., and NORMAN, M. G. “A ‘Memetic’ Approach for the Traveling Salesman Problem,” Proceedings of the International Conference on Parallel Computing and Transputer Applications, IOS Press, Amsterdam, 1992, pp. 187–194.
DAWKINS, R. The Selfish Gene, Oxford University Press, Oxford, 1976.
WHITLEY, D., GORDON, S., and MATHIAS, K. “Lamarckian Evolution, The Baldwin Effect and Function Optimization,” Parallel Problem Solving from Nature-PPSN III, edited by Y. Davidor, H. Schwefel, and R. Manner, Springer-Verlag, 1994, pp. 6–15.
DEB, K., and GOLDBERG, D. E. “An Investigation of Niche and Species Formation in Genetic Function Optimization,” Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, California, 1989, pp. 42–50.
WILLIAMS, S., and COVERSTONE-CARROLL, V. “Benefits of Solar Electric Propulsion for the Next Generation of Planetary Exploration Missions,” Journal of the Astronautical Sciences, Vol. 45, No. 2, April–June 1997, pp. 143–159.
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Hartmann, J.W., Coverstone-Carroll, V.L. & Williams, S.N. Optimal Interplanetary Spacecraft Trajectories via a Pareto Genetic Algorithm. J of Astronaut Sci 46, 267–282 (1998). https://doi.org/10.1007/BF03546237
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DOI: https://doi.org/10.1007/BF03546237