Multi-objective optimization of reconnaissance constellation based on machine learning

Yali Liu; Wei Xiong; Minghui Xiong; Xiaolan Yu; Zheng Liu

doi:10.1117/12.3009219

11 October 2023 Multi-objective optimization of reconnaissance constellation based on machine learning

Yali Liu, Wei Xiong, Minghui Xiong, Xiaolan Yu, Zheng Liu

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180J (2023) https://doi.org/10.1117/12.3009219
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

The optimization of reconnaissance constellation is an important step in the construction of space-based information system, and the coverage performance is the key index of the optimization of reconnaissance constellation. However, the coverage performance of the current constellation is mostly obtained through the simulation system, which has high time complexity and low efficiency, seriously slowing down the speed of constellation optimization. In order to improve the efficiency of constellation optimization, a two-stage optimization scheme of reconnaissance constellation based on machine learning is proposed. First, the trained machine learning prediction model is combined with the evolutionary algorithm to carry out rough optimization of the reconnaissance constellation, and the decision variable space after the scope is reduced is obtained. Then, in the reduced decision variable space, the simulation system and evolutionary algorithm are used to further optimize the problem, and the final result is obtained. The scheme proposed in this paper speeds up the convergence of population by reducing the search space of decision variables, thus improving the optimization efficiency. By comparing the optimization results of this scheme with those of the traditional scheme, it is found that the proposed scheme is 78.21% shorter than that of the traditional scheme under the premise that the quality gap between the optimization results of the traditional scheme and the traditional scheme is acceptable, which greatly improves the optimization speed of the reconnaissance constellation, and verifies the effectiveness and advancement of the proposed scheme.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yali Liu, Wei Xiong, Minghui Xiong, Xiaolan Yu, and Zheng Liu "Multi-objective optimization of reconnaissance constellation based on machine learning", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180J (11 October 2023); https://doi.org/10.1117/12.3009219

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KEYWORDS

Reconnaissance

Mathematical optimization

Data modeling

Performance modeling

Education and training

Machine learning

Evolutionary optimization

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