Time Slot Allocation Method of Data Link Based on Improved Difference Algorithm

doi:10.16182/j.issn1004731x.joss.23-0095

Abstract

Abstract:

Aiming at the problems of single algorithm, being prone to local optima, and weak generalization ability of the current strategies, based on an improved differential evolutionary algorithm, a chaos algorithm, an adaptive variational crossover algorithm, and a problem solution processing mechanism, a time slot allocation strategy is proposed. The chaos algorithm is used to initialize the population to increase the diversity and avoid the premature convergence. The selection probability parameter is then used to make the crossover and variation process more flexible, expanding the search range in early to increase the possibility of global optima in late. The experiment results show that the proposed algorithm outperforms the difference algorithm and genetic algorithm in all four indicators of time slot allocation balance, stability, algorithm efficiency, and generalization ability. The proposed algorithm achieves higher time slot allocation balance and algorithm efficiency, better stability, and stronger generalization ability.

Key words: tactical data link, slot allocation, differential evolution algorithm, chaotic mapping, time slot variance

CLC Number:

TP391.9

Zhu Yuting, Su Huankun, Feng Xiaodong, Lei Shijie, Fu Yanfang. Time Slot Allocation Method of Data Link Based on Improved Difference Algorithm[J]. Journal of System Simulation, 2024, 36(5): 1242-1250.

Figures/Tables 14

Fig. 1

Fig. 2

Table 1

Common variation strategies

算法名称	策略
DE/rand/1	$W i G + 1 = x r 1 G + F ⋅ (x r 2 G - x r 3 G)$
DE/rand/2	$W i G + 1 = x r 1 G + F ⋅ (x r 2 G - x r 3 G) + F ⋅ (x r 4 G - x r 5 G)$
DE/rand-to-best/1	$W i G + 1 = x r 1 G + F ⋅ (x r 2 G - x r 3 G) + F ⋅ (x r 4 G - x r 5 G)$
DE/rand-to-best/2	$W i G + 1 = x r 1 G + F ⋅ (x r 2 G - x r 3 G) + F ⋅ (x r 4 G - x r 5 G)$
DE/current-to-best/1	$W i G + 1 = x r 1 G + F ⋅ (x r 2 G - x r 3 G) + F ⋅ (x r 4 G - x r 5 G)$

Table 1

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 2

Benchmark functions

函数名	表达式	搜索范围
Schwefel	$f 8 (x) = ∑ i = 1 D - x i s i n \| x i \|$	[-500, 500]
Rastrigin	$f 9 (x) = ∑ i = 1 D [x i 2 - 10 c o s (2 π x i) + 10]$	[-5.12, 5.12]

Table 2

Table 3

Fig. 10

Fig. 11

References 14

1	吕娜. 数据链理论与系统[M]. 北京: 电子工业出版社, 2011.
	Na Lü. Theory and System of Data Link[M]. Beijing: Publishing House of Electronics Industry, 2011.
2	梅文华, 蔡善法. JTIDS/Link16数据链[M]. 北京: 国防工业出版社, 2007.
3	王文政. 战术数据链时隙分配协议及其仿真研究[D]. 长沙: 国防科学技术大学, 2010.
	Wang Wenzheng. Study on Slot Assignment Protocol and Its Simulation for Tactical Data Links[D]. Changsha: National University of Defense Technology, 2010.
4	刘星, 何肇雄, 周经伦. TDMA战术数据链中最小时延抖动的固定时隙分配算法[J]. 系统工程, 2012, 30(6): 90-94.
	Liu Xing, He Zhaoxiong, Zhou Jinglun. A Minimum-jitter Fixed Timeslot Allocation Algorithm for TDMA Tactical Data Link[J]. Systems Engineering, 2012, 30(6): 90-94.
5	陈嘉远, 钟章队, 刘强. 基于遗传算法的TDMA战术数据链时隙分配算法[J]. 火力与指挥控制, 2015, 40(8): 97-100.
	Chen Jiayuan, Zhong Zhangdui, Liu Qiang. Time Slot Allocation Algorithm for TDMA Tactical Data Link Based on Genetic Algorithms[J]. Fire Control & Command Control, 2015, 40(8): 97-100.
6	傅妍芳, 张楠, 魏佳宁, 等. 基于OPNET的机群数据链混合TDMA协议仿真[J]. 系统仿真学报, 2022, 34(9): 1933-1940.
	Fu Yanfang, Zhang Nan, Wei Jianing, et al. OPNET Based Simulation of Hybrid TDMA Protocol for Helicopters Datalink[J]. Journal of System Simulation, 2022, 34(9): 1933-1940.
7	王瑞, 孙晓伟, 毛忠阳, 等. 基于遗传禁忌搜索的时隙分配算法[J]. 电子测量技术, 2022, 45(10): 82-86.
	Wang Rui, Sun Xiaowei, Mao Zhongyang, et al. Slot Allocation Algorithm Based on Genetic and Tabu Search[J]. Electronic Measurement Technology, 2022, 45(10): 82-86.
8	张春美, 陈杰, 辛斌. 武器目标分配问题的离散差分进化算法[J]. 北京理工大学学报, 2014, 34(3): 289-293, 321.
	Zhang Chunmei, Chen Jie, Xin Bin. A Discrete Differential Evolution Algorithm for the Weapon Target Assignment Problem[J]. Transactions of Beijing Institute of Technology, 2014, 34(3): 289-293, 321.
9	马悦, 吴琳, 郭圣明. 基于改进差分进化算法的作战目标分配[J]. 指挥控制与仿真, 2022, 44(4): 31-41.
	Ma Yue, Wu Lin, Guo Shengming. Operational Targets Assignment Based on Improved DE Algorithm[J]. Command Control & Simulation, 2022, 44(4): 31-41.
10	鲁军, 李鹏飞. 基于差分进化算法的雷达网目标分配方法研究[J]. 舰船电子工程, 2022, 42(8): 29-32, 54.
	Lu Jun, Li Pengfei. Research on Radar Network Target Assignment Method Based on Differential Evolution Algorithm[J]. Ship Electronic Engineering, 2022, 42(8): 29-32, 54.
11	赵志勇, 毛忠阳, 张嵩, 等. 数据链系统与技术[M]. 北京: 电子工业出版社, 2014.
	Zhao Zhiyong, Mao Zhongyang, Zhang Song, et al. Data Link System and Technology[M]. Beijing: Publishing House of Electronics Industry, 2014.
12	丁青锋, 尹晓宇. 差分进化算法综述[J]. 智能系统学报, 2017, 12(4): 431-442.
	Ding Qingfeng, Yin Xiaoyu. Research Survey of Differential Evolution Algorithms[J]. CAAI Transactions on Intelligent Systems, 2017, 12(4): 431-442.
13	Tian Mengnan, Gao Xingbao, Dai Cai. Differential Evolution with Improved Individual-based Parameter Setting and Selection Strategy[J]. Applied Soft Computing, 2017, 56: 286-297.
14	孙凤山, 范孟豹, 曹丙花, 等. 基于混沌映射与差分进化自适应教与学优化算法的太赫兹图像增强模型[J]. 仪器仪表学报, 2021, 42(4): 92-101.
	Sun Fengshan, Fan Mengbao, Cao Binghua, et al. The Terahertz Image Enhancement Model Based on Adaptive Teaching-learning Based Optimization Algorithm with Chaotic Mapping and Differential Evolution[J]. Chinese Journal of Scientific Instrument, 2021, 42(4): 92-101.

[1]	Luo Tianyu, Xing Lining, Wang Rui, Wang Ling, Shi Jianmai, Sun Xin. Dynamic Air Defense Resource Allocation Optimization Based on Improved Differential Evolution Algorithm [J]. Journal of System Simulation, 2024, 36(6): 1285-1297.
[2]	Li Gaoyang, Li Xiangfeng, Zhao Kang, Jin Yuchao, Yi Zhidong, Zuo Dunwen. Three-Dimensional Path Planning of UAV Based on All Particles Driving Wild Horse Optimizer Algorithm [J]. Journal of System Simulation, 2024, 36(3): 595-607.
[3]	Qiang Chen, Yi Wang, Kangshun Li. Research on Modeling and Scheduling of Virtual Power Plant with Dual Demand Response [J]. Journal of System Simulation, 2023, 35(4): 822-832.
[4]	Yanfang Fu, Nan Zhang, Jianing Wei, Shaochun Qu, Ying Lu, Chang Liu. OPNET Based Simulation of Hybrid TDMA Protocol for Helicopters Datalink [J]. Journal of System Simulation, 2022, 34(9): 1933-1940.
[5]	Yu Wei, Liang Henghui, Luo Ying. An Improved Differential Evolution Algorithm for Fractional Order System Identification [J]. Journal of System Simulation, 2021, 33(5): 1157-1166.
[6]	Mei Panpan, Wu Lianghong, Zhang Hongqiang, Wang Huiying. Adaptive Differential Crisscross Optimization Algorithm for Dynamic Economic Emission Dispatch Considering Wind Power [J]. Journal of System Simulation, 2020, 32(6): 1179-1187.
[7]	Wang Yan, Ding Yu. Optimal Scheduling and Decision Making Method for Dynamic Flexible Job Shop [J]. Journal of System Simulation, 2020, 32(11): 2073-2083.
[8]	Lin Yugu, Wang Yan. Energy Efficiency Data Mining and Scheduling Optimization of Discrete Workshop [J]. Journal of System Simulation, 2019, 31(12): 2702-2711.
[9]	Wang Jie, Chen Bin, Yuan Peng, Ma Liang, Qiu Xiaogang. Study on Data-driven ORCA Preference Velocity [J]. Journal of System Simulation, 2019, 31(12): 2731-2739.
[10]	Tao Wenhua, Yuan Zhengbo. Prediction Model of Coke Quality Based on DE-BP Neural Network [J]. Journal of System Simulation, 2018, 30(5): 1650-1656.
[11]	Xiong Yinguo. Prediction Model for Breakdown Voltage of Transformer Oil Based on Relative Transformation and Kernel Principal Component Analysis [J]. Journal of System Simulation, 2018, 30(5): 1657-1664.
[12]	Zhang Jinghua, Han Pu. Multi-algorithm and Multi-population Co-optimization Differential Evolution Algorithm [J]. Journal of System Simulation, 2018, 30(5): 1690-1699.
[13]	Chen Wenjie, Wang Yan. Collaborative Optimal Scheduling Method for Production and Energy Consumption in Discrete Manufacturing Process [J]. Journal of System Simulation, 2018, 30(11): 4367-4375.
[14]	Li Qin, Zhang Hao, Peng Daogang, Guo Yibo, Wang Nianlong, Sun Yuzhen. Multi-variable Modeling Research for Main-steam Temperature of Power Station Boiler Based on Improved Differential Evolution Algorithm [J]. Journal of System Simulation, 2017, 29(8): 1712-1718.
[15]	Zhang Yan, Wang Dongfeng, Han Pu. Short-term Prediction of Wind Speed for Wind Farm Based on IDE-LSSVM Model [J]. Journal of System Simulation, 2017, 29(7): 1561-1571.