TY - JOUR
T1 - Deep-space trajectory optimizations using differential evolution with self-learning
AU - Choi, Jin Haeng
AU - Lee, Jinah
AU - Park, Chandeok
N1 - Publisher Copyright:
© 2021 IAA
PY - 2022/2
Y1 - 2022/2
N2 - This paper presents spacecraft trajectory optimizations for deep-space missions requiring multiple gravity-assists (MGA). The main algorithm is based on a self-adaptive/self-learning differential evolution (DE). In the process of improving the performance of DE for optimizing the MGA trajectory, the proposed algorithm alleviates the dependence on predetermined mutation strategy and control parameters in DE; as evolution progresses, the mutation strategy and the control parameters switch adaptively to more promising ones by reflecting experiences in previous evolution steps. Furthermore, the proposed algorithm is equipped with a re-initialization technique to directly mollify the issue of converging to a local optimum, which is often observed when optimizing the MGA trajectory. In order to demonstrate these favorable characteristics, the proposed algorithm is implemented to solve six well-known MGA trajectory optimization problems designed by the European space agency (ESA). Compared with the latest representative evolutionary algorithms, the proposed algorithm can successfully converge to the currently known best solutions of five MGA problems; our solutions to four of those MGA problems are better than currently known solutions. The proposed algorithm also performs well as a local/auxiliary search algorithm to improve the performance of other evolutionary algorithms. In addition to describing the algorithms and solutions characteristics, sensitivity analysis is presented to quantitatively investigate the search capability of finding the optimal solutions of MGA problems. The overall results show that our self-learning DE is competitively compared with other representative algorithms in terms of convergences to the global optimum, reliable search capability, and applicability to a variety of deep-space trajectory optimizations.
AB - This paper presents spacecraft trajectory optimizations for deep-space missions requiring multiple gravity-assists (MGA). The main algorithm is based on a self-adaptive/self-learning differential evolution (DE). In the process of improving the performance of DE for optimizing the MGA trajectory, the proposed algorithm alleviates the dependence on predetermined mutation strategy and control parameters in DE; as evolution progresses, the mutation strategy and the control parameters switch adaptively to more promising ones by reflecting experiences in previous evolution steps. Furthermore, the proposed algorithm is equipped with a re-initialization technique to directly mollify the issue of converging to a local optimum, which is often observed when optimizing the MGA trajectory. In order to demonstrate these favorable characteristics, the proposed algorithm is implemented to solve six well-known MGA trajectory optimization problems designed by the European space agency (ESA). Compared with the latest representative evolutionary algorithms, the proposed algorithm can successfully converge to the currently known best solutions of five MGA problems; our solutions to four of those MGA problems are better than currently known solutions. The proposed algorithm also performs well as a local/auxiliary search algorithm to improve the performance of other evolutionary algorithms. In addition to describing the algorithms and solutions characteristics, sensitivity analysis is presented to quantitatively investigate the search capability of finding the optimal solutions of MGA problems. The overall results show that our self-learning DE is competitively compared with other representative algorithms in terms of convergences to the global optimum, reliable search capability, and applicability to a variety of deep-space trajectory optimizations.
KW - Deep-space trajectory
KW - Differential evolution
KW - Multiple gravity assist
KW - Trajectory optimization
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U2 - 10.1016/j.actaastro.2021.11.014
DO - 10.1016/j.actaastro.2021.11.014
M3 - Article
AN - SCOPUS:85119929038
SN - 0094-5765
VL - 191
SP - 258
EP - 269
JO - Acta Astronautica
JF - Acta Astronautica
ER -