On-orbit Refueling Mission Planning of Satellites Based on Improved NSGA-II

doi:10.16182/j.issn1004731x.joss.25-1198

Abstract

Abstract:

An improved NSGA-II solution framework based on multi-tree encoding is proposed to address the problems of high modeling complexity under large-scale scenarios and the tendency of traditional algorithms to fall into local optima. Based on multi-tree modeling, the complex mixed-integer programming problem is transformed into a multi-tree configuration optimization problem, thus reducing the constraint handling difficulty and improving the search efficiency of the solution space. Multi-tree crossover and mutation operators as well as the calculation method for objective functions are designed. A large neighborhood search (LNS) strategy is introduced, and the global exploration and local exploitation capabilities of the algorithm are balanced through a "destruction-repair" mechanism. Simulation results demonstrate that under scenarios of different scales, the improved algorithm outperforms classic multi-objective evolutionary algorithms including NSGA-II, MOEA/D and SPEA2 in terms of HV and IGD metrics.

Key words: satellite, on-orbit refueling, multi-tree, LNS-NSGA-II, mission planning

CLC Number:

TP391

Li Guozheng, Wang Rui, Lu Yi. On-orbit Refueling Mission Planning of Satellites Based on Improved NSGA-II[J]. Journal of System Simulation, 2026, 38(10): 3113-3125.

Figures/Tables 29

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Table 1

Theoretical analysis of advantages of multi-tree encoding

对比	序列编码	多叉树编码
解空间增长率	$O ((n!) k)$	$O (B n ⋅ n!)$
无效解比例	高	低
结构信息保留	无	完整
C1、C2访问唯一性	需要修复算子	树结构天然保证
C3往返次数	需要额外编码	子树数量直接表示
破坏操作	$O (n)$	$O (l o g n)$
修复操作	$O (n 2)$	$O (n l o g n)$
可行性检验	$O (n 2)$	$O (n)$

Table 1

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参数	场景规模
参数	10	30	50	100
种群大小	50	100	150	200
迭代次数	50	100	150	150
交叉概率	0.9	0.9	0.9	0.9
变异概率	0.1	0.1	0.1	0.1
破坏精英个体比例	0.2	0.2	0.2	0.2

名称	相位/rad	燃料需求/kg	名称	相位/rad	燃料需求/kg
T1	0.16	227.89	T16	4.59	261.43
T2	1.4	155.01	T17	6.11	207.25
T3	4.25	247.29	T18	3.47	175.71
T4	0.55	278.44	T19	3.89	265.88
T5	0.19	184.38	T20	3.63	272.34
T6	3.18	143.73	T21	0.29	240.91
T7	1.25	105.31	T22	1.82	145.58
T8	3.42	229.98	T23	1.46	115.96
T9	3.7	144.09	T24	1.75	120.20
T10	0.04	261.89	T25	2.29	227.14
T11	4.39	261.16	T26	1.32	174.04
T12	0.98	168.05	T27	5.89	153.40
T13	2.11	291.44	T28	3.83	229.61
T14	0.61	118.55	T29	4.58	134.23
T15	3.79	269.50	T30	2.38	132.68