基于自适应模拟退火的大规模星座测控资源调度算法

doi:10.7527/S1000-6893.2022.27759

Abstract

Abstract:

With the rapid growth of the number of satellites in orbit， the already relatively scarce Tracking Telemetry and Command （TT&C） resources are becoming even more scarce. The TT&C resource scheduling problem for large-scale constellation is restricted by multiple constraints， and is a complex combinatorial optimization problem with strong conflicts. An improved adaptive simulated annealing algorithm is proposed to solve the TT&C resource scheduling problem for large-scale constellation in order to improve the capability of TT&C systems and the revenue from completing TT&C tasks. Firstly， the satellite TT&C process is analyzed. According to satellite TT&C task requirements and resource usage constraints， a constraint satisfaction model is established aiming to maximize the total profits from task completion. A method for evaluating the conflict degree of available TT&C opportunities is proposed， and a fitness based task allocation algorithm is designed to generate a high-quality initial solution. Then， an adaptive simulated annealing algorithm combining perturbation strategy and tabu mechanism is designed， which adaptively controls the update of temperature and neighborhood structure selection probability during the optimization process of the algorithm. The short-term memory mechanism of the tabu table is used to avoid repeated searches. The perturbation strategy is combined to diverse the solution to a certain extent， thereby jumping out of the local optima and enhancing the optimization performance of the algorithm. Finally， in order to verify the effectiveness of the proposed method， a large number of simulation experiments were conducted to compare the algorithm with simulated annealing algorithm， genetic algorithm， fitness based task allocation algorithm， and maximum weight first allocation algorithm. Experimental results show that compared with traditional algorithms， the proposed algorithm improves the solution by 10.34%， 23.59%， 23.20%， and 46.51%， respectively.

Key words: large-scale constellation, TT&C resource scheduling, conflict degree, simulated annealing algorithm, self-adaptive

CLC Number:

Guohua WU, Tianyu WANG. Large-scale constellation TT&C resource scheduling algorithm based on adaptive simulated annealing[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(12): 327759-327759.

Figures/Tables 25

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Table 1

Table 2

Parameters of ASATP

参数	数值
温度的最小值 $θ m$	0.3
温度的权重系数 $μ$	1
温度的改变系数 $λ$	10
惯性权重 $ω$	0.6
邻域概率更新的迭代次数NItr	15
局部扰动的迭代次数PItr	10
禁忌表长度L	N/100
最大迭代次数MaxItr	10N
最优解的目标函数值没有改进的连续迭代次数MaxConItr	N/10

Table 2

Table 3

Candidate values of parameters

算法参数	取值范围
$θ m$	${0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9}$
$μ$	${0.1,0.2,0.5,0.8,1, 1.2,1.5,1.8,2}$
$λ$	${5,6, 7,8, 9,10,11,12,13,14,15}$
$ω$	${0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9}$
$N I t r$	${2,4, 6,8, 10,12,15,18,20}$
$P I t r$	${2,4, 6,8, 10,12,15,18,20}$

Table 3

Fig.15

Table 4

Table 5

Table 6

Comparison of solution results of various algorithms with different scales

实验编号	HWFA			TAAFV			GA			SA			ASATP			t-test
实验编号	Avg	Std	Times/s	Avg	Std	Times/s	Avg	Std	Times/s	Avg	Std	Times/s	Avg	Std	Times/s	t-test
Case1	2 520.7	36.94	0.009 4	2 938.7	39.48	0.043 6	3 097.9	24.32	31.044 9	3 253.0	0.00	0.043 3	3 253.0	0.00	0.093 4	+
Case2	3 628.9	48.24	0.016 1	4 316.6	47.12	0.096 4	4 452.1	31.31	64.261 5	5 223.1	25.84	0.169 7	5 355.9	19.73	0.556 0	+
Case3	4 422.1	44.75	0.023 3	5 317.6	51.65	0.165 2	5 314.5	32.08	102.321 0	6 107.4	35.43	0.211 7	6 654.5	46.43	1.119 3	+
Case4	5 175.1	75.01	0.031 8	6 182.8	42.96	0.239 8	6 070.5	36.51	146.884 2	6 797.1	55.00	0.247 2	7 665.9	75.62	1.575 3	+
Case5	5 599.6	62.52	0.040 9	6 635.8	52.07	0.320 3	6 440.4	51.79	182.731 3	7 090.5	44.71	0.280 9	8 290.2	53.22	2.414 2	+
Case6	2 531.4	33.02	0.009 5	2 968.8	34.17	0.043 4	3 142.6	21.29	38.255 8	3 355.0	0.00	0.050 9	3 355.0	0.00	0.099 6	+
Case7	3 331.3	35.11	0.014 6	3 953.4	43.27	0.085 1	4 103.7	35.36	66.788 5	4 786.7	16.83	0.178 6	4 826.8	8.49	0.464 6	+
Case8	4 193.3	47.17	0.020 4	5 043.0	54.88	0.130 2	5 110.9	54.65	97.668 9	5 947.4	24.45	0.216 2	6 354.4	49.27	0.812 9	+
Case9	4 600.8	76.44	0.026 0	5 533.4	62.14	0.184 1	5 497.9	49.09	131.269 3	6 282.5	37.85	0.237 4	6 916.9	61.47	1.154 4	+
Case10	5 169.9	81.26	0.032 4	6 162.0	51.51	0.244 8	6 037.9	34.59	166.942 2	6 768.6	39.63	0.267 5	7 711.3	64.90	1.779 8	+
Case11	5 621.0	80.41	0.039 4	6 632.1	55.63	0.325 5	6 449.4	40.44	243.349 5	7 065.3	53.9	0.298 2	8 258.1	90.01	2.313 8	+
Case12	5 940.6	79.95	0.047 4	6 976.3	69.88	0.397 0	6 768.1	40.43	248.744 1	7 277.5	67.11	0.325 1	8 556.3	83.45	2.426 8	+
Case13	2 542.8	35.47	0.009 5	2 972.3	27.56	0.043 2	3 145.1	21.24	30.201 0	3 301.0	0.00	0.039 7	3 301.0	0.00	0.097 3	+
Case14	3 654.6	42.35	0.016 1	4 408.5	40.78	0.096 2	4 514.5	38.32	63.870 5	5 310.2	26.95	0.170 4	5 460.7	21.61	0.591 9	+
Case15	4 610.7	77.17	0.023 9	5 515.5	52.03	0.163 4	5 503.2	40.77	102.720 8	6 354.4	67.42	0.210 7	6 866.2	45.55	1.052 7	+
Case16	5 142.0	72.55	0.031 4	6 145.0	42.06	0.239 2	6 015.3	47.48	146.184 1	6 745.8	52.81	0.245 1	7 654.1	60.33	1.815 2	+
Case17	5 569.1	63.77	0.037 9	6 662.6	54.73	0.314 8	6 428.5	71.28	182.681 3	7 081.9	60.89	0.275 2	8 129.2	97.97	2.127 1	+
Case18	2 504.4	32.69	0.009 6	2 922.5	30.50	0.043 3	3 095.7	22.92	37.934 4	3 317.0	0.00	0.050 3	3 317.0	0.00	0.104 0	+
Case19	3 431.4	45.69	0.014 7	4 063.5	49.33	0.083 6	4 206.5	30.47	66.694 7	4 914.5	18.91	0.178 1	4 957.8	7.54	0.422 4	+
Case20	4 084.8	77.68	0.020 2	4 937.5	49.03	0.131 1	4 988.9	34.52	97.671 0	5 802.2	26.40	0.206 1	6 113.1	41.93	0.734 3	+
Case21	4 681.1	70.10	0.026 1	5 598.2	47.48	0.185 0	5 570.2	44.24	131.433 8	6 392.2	47.77	0.241 7	7 029.9	42.57	1.186 7	+
Case22	5 072.1	68.20	0.032 4	6 046.2	54.56	0.244 8	5 949.4	42.56	167.252 5	6 665.4	50.41	0.265 8	7 693.3	116.89	1.805 0	+
Case23	5 444.8	85.77	0.038 6	6 417.7	43.35	0.320	6 287.7	52.38	221.200 8	6 817.9	68.51	0.299 2	8 120.9	78.22	2.391 6	+
Case24	5 751.4	51.60	0.045 6	6 783.2	30.82	0.393 7	6 552.4	49.51	264.016 6	7 067.2	68.47	0.324 2	8 326.7	144.29	2.479 9	+

Table 6

Table 7

Wilcoxon signed-rank tests of ASATP and other algorithms

算法对比	$R +$	$R -$	p-value
ASATP vs SA	295	5	0.000 088 58
ASATP vs GA	300	0	0.000 018 22
ASATP vs TAAFV	300	0	0.000 018 22
ASATP vs HWFA	300	0	0.000 018 22

Table 7

Fig.16

Fig.17

Fig.18

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实验编号	Task	Sat	Station	Distribution
Case1	600	600	9	均匀分布
Case2	1 000	1 000	9	均匀分布
Case3	1 400	1 400	9	均匀分布
Case4	1 800	1 800	9	均匀分布
Case5	2 100	2 100	9	均匀分布
Case6	600	200	9	均匀分布
Case7	900	300	9	均匀分布
Case8	1 200	400	9	均匀分布
Case9	1 500	500	9	均匀分布
Case10	1 800	600	9	均匀分布
Case11	2 100	700	9	均匀分布
Case12	2 400	800	9	均匀分布
Case13	6 00	600	9	密集分布
Case14	1 000	1 000	9	密集分布
Case15	1 400	1 400	9	密集分布
Case16	1 800	1 800	9	密集分布
Case17	2 100	2 100	9	密集分布
Case18	600	200	9	密集分布
Case19	900	300	9	密集分布
Case20	1 200	400	9	密集分布
Case21	1 500	500	9	密集分布
Case22	1 800	600	9	密集分布
Case23	2 100	700	9	密集分布
Case24	2 400	800	9	密集分布

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Large-scale constellation TT&C resource scheduling algorithm based on adaptive simulated annealing

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