Hybrid Jaya-Transformer-based Task Scheduling Algorithm for Super-agile Satellites

doi:10.16182/j.issn1004731x.joss.26-0395

Abstract

Abstract:

Super-agile satellite task scheduling is characterized by complex constraints and tight coupling among task transitions. To address the relatively long online solution times of traditional heuristic algorithms, this paper establishes a super-agile satellite task scheduling model that aims to maximize the task profit ratio while considering constraints on visible time windows, attitude transition time, energy, and storage capacity. A Transformer is used to learn task requirements, visible time windows, and global correlations among tasks, and a dynamic mask and a task scheduler are incorporated to generate feasible task sequences. To enhance the model's ability to learn complex task correlations and reduce the influence of manual hyperparameter tuning on algorithm performance, this paper proposes a hybrid Jaya-Transformer scheduling algorithm. Experimental results show that the hybrid Jaya-Transformer improves the average task profit ratio by 2.58-11.89 percentage points compared with Transformer. Compared with ALNS and IGA-LS, the proposed algorithm achieves optimal or near-optimal task profit ratios with an average solution time of only 0.21-0.78 s, substantially shorter than the 6.21-108.9 s required by the heuristic algorithms, demonstrating its ability to balance scheduling quality and solution efficiency.

Key words: super-agile satellite, Transformer, hyperparameter optimization, hybrid Jaya algorithm, task scheduling

CLC Number:

TP391.9

Zhao Haoyu, Zhang Guanghui, Hao Lanting, Guo Meijia. Hybrid Jaya-Transformer-based Task Scheduling Algorithm for Super-agile Satellites[J]. Journal of System Simulation, 2026, 38(10): 3052-3067.

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卫星参数	值
半长轴/km	6 678.14
偏心率	0
轨道倾角/(°)	97.406 5
升交点赤经/(°)	120
近地点幅角/(°)	0
真近点角/(°)	34
时间	2025-04-20T04:00:00—21T04:00:00

任务数目	算法	任务收益率/%			平均求解时间/s
任务数目	算法	最小值	最大值	平均值	平均求解时间/s
50	ALNS	99.21	100	99.69	8.34
	IGA-LS	96.85	100	99.22	6.21
	Transformer	96.55	97.68	97.07	0.20
	本文	98.26	100	99.65	0.21
100	ALNS	90.58	94.38	92.32	29.74
	IGA-LS	85.58	94.57	90.34	24.34
	Transformer	87.23	89.94	88.03	0.40
	本文	89.10	94.94	93.24	0.45
150	ALNS	80.25	84.65	82.33	67.43
	IGA-LS	75.35	83.40	79.52	67.80
	Transformer	69.24	72.36	71.27	0.59
	本文	81.73	85.68	83.16	0.61
200	ALNS	70.98	75.23	72.48	106.42
	IGA-LS	63.31	74.68	70.71	108.90
	Transformer	63.92	65.22	64.41	0.84
	本文	70.33	77.41	74.36	0.78

任务数目	算法	标准差/%	p-value	显著性
50	ALNS	0.25	0.556 4	ns
	IGA-LS	1.33	0.345 2	ns
	Transformer	0.37	0.002 9	**
	本文	0.62	--	--
100	ALNS	1.27	0.042	*
	IGA-LS	2.67	0.037 1	*
	Transformer	0.80	0.002 9	**
	本文	1.66	--	--
150	ALNS	1.63	0.065 4	ns
	IGA-LS	2.32	0.013 7	*
	Transformer	1.00	0.002 9	**
	本文	1.21	--	--
200	ALNS	1.34	0.037 1	*
	IGA-LS	3.60	0.037 1	*
	Transformer	0.44	0.002 9	**
	本文	2.07	--	--