ZHANG Chenlu, PENG Dongliang, FANG Tao, GU Yu. Resource Scheduling Method for Space-Based Early Warning System Based on GA-PSO Algorithm[J]. INFORMATION AND CONTROL, 2016, 45(2): 199-203,210. DOI: 10.13976/j.cnki.xk.2016.0199
Citation: ZHANG Chenlu, PENG Dongliang, FANG Tao, GU Yu. Resource Scheduling Method for Space-Based Early Warning System Based on GA-PSO Algorithm[J]. INFORMATION AND CONTROL, 2016, 45(2): 199-203,210. DOI: 10.13976/j.cnki.xk.2016.0199

Resource Scheduling Method for Space-Based Early Warning System Based on GA-PSO Algorithm

More Information
  • Received Date: January 22, 2015
  • Revised Date: July 12, 2015
  • Available Online: December 07, 2022
  • Published Date: April 19, 2016
  • To solve the resource scheduling problem of satellites in space-based early warning system, we build a resource scheduling model based on the characteristics of the early warning tasks and their decomposition. We propose a hybrid GA-PSO optimization algorithm, which combines the advantages of genetic algorithm (GA) and particle swarm optimization (PSO), to solve the resource scheduling problem for the space-based early warning system. The algorithm introduces the genetic operators of GA into PSO algorithm to improve the search ability of PSO algorithm, while solving the problem of particles coding and decoding. The experimental results demonstrate that the proposed algorithm can solve resource scheduling problem of space-based early warning system for multi-target detection effectively and space-based early warning missions, and the scheduling results is better than GA and PSO.
  • [1]
    刘波. 美国天基预警系统现状与发展[J]. 战术导弹术, 2011, 8(3): 118-123.

    Liu B. Status and development of American space-based early warning system[J]. Tactical Missile Technology, 2011, 8(3): 118-123.
    [2]
    赵阳, 易先清, 罗雪山. 面向任务的天基预警系统应用体系结构研究[J]. 中国电子科学研究院学报, 2008(3): 247-251.

    Zhao Y, Yi X Q, Luo X S. Research on the application architecture of the space-based early warning system task-oriented[J]. Journal of China Academy of Electronics and Information Technology, 2008(3): 247-251.
    [3]
    姜维, 李一军. 天基预警调度方法研究[J]. 系统工程理论与实践, 2012, 32(9): 2065-2077.

    Jiang W, Li Y J. The scheduling model and algorithm of space based early warning[J]. Systems Engineering - Theory & Practice, 2012, 32(9): 2065-2077.
    [4]
    罗开平, 李一军. 导弹预警卫星调度问题分析[J]. 现代防御技术, 2009, 37(6): 5-10.

    Luo K P, Li Y J. Analysis of the early warning satellite scheduling problem[J]. Modern Defence Technology, 2009, 37(6): 5-10.
    [5]
    Sarkheyli A, Vaghei B G, Moghadam R A, et al. Scheduling earth observation activities in LEO satellites using graph coloring problem[C]//IEEE International Symposium on Telecommunications (IST). Piscataway, NJ, USA: IEEE, 2010: 928-931.
    [6]
    Wang H, Xu M, Wang R, et al. Scheduling earth observing satellites with hybrid ant colony optimization algorithm[C]//IEEE International Conference on Artificial Intelligence and Computational Intelligence. Piscataway, NJ, USA: IEEE, 2009: 245-249.
    [7]
    阎志伟, 牛轶峰, 李汉铃. 基于并行禁忌遗传算法 (PTGA)的预警卫星传感器调度研究[J]. 宇航学报, 2003, 24(6): 598-603.

    Yan Z W, Niu Y F, Liu H L. Study of sensor scheduling for early warning satellite based on parallel tabu genetic algorithm (PTGA). Journal of Astronautics, 2003, 24(6): 598-603.
    [8]
    郭浩波, 王颖龙, 曾辉. 采用遗传模拟退火算法研究弹预警卫星传感器调度[J]. 电光与控制, 2006, 13(4): 71-74.

    Guo H B, Wang Y L, Zeng H. Sensor scheduling for missile early-warning satellite based on genetic and simulated annealing algorithm[J]. Electronics Optics & Control, 2006, 13(4): 71-74.
    [9]
    冯明月, 汤绍勋, 何俊, 等. 基于改进粒子群算法的天基预警系统资源调度方法[J]. 中国电子科学研究院学报, 2010, 5(1): 97-101.

    Feng M Y, Tang S X, He J, et al. Resource scheduling for space-based early warning system based on improved particle swarm optimization[J]. Journal of China Academy of Electronics and Information Technology, 2010, 5(1): 97-101.
    [10]
    葛继科, 邱玉辉, 吴春明, 等. 遗传算法研究综述[J]. 计算机应用研究, 2008, 25(10): 2911-2916.

    Ge J K, Qiu Y H, Wu C M, et al. Summary of genetic algorithms research[J]. Application Research of Computers, 2008, 25(10): 2911-2916.
    [11]
    马永杰, 云文霞. 遗传算法研究进展[J]. 计算机应用研究, 2012, 29(4): 1201-1206, 1210.

    Ma Y Z, Yun W X. Research progress of genetic algorithm[J]. Application Research of Computers, 2012, 29(4): 1201-1206, 1210.
    [12]
    杨维, 李歧强. 粒子群优化算法综述[J]. 中国工程科学, 2004, 6(5): 87-94.

    Yang W, Li Q Q. Particle swarm optimization algorithm[J]. Engineering Science, 2004, 6(5): 87-94.
    [13]
    高卫峰, 刘三阳. 一种高效粒子群优化算法[J]. 控制与决策, 2011, 26(8): 1158-1162.

    Gao W F, Liu S Y. An efficient particle swarm optimization[J]. Control and Decision, 2011, 26(8): 1158-1162.
    [14]
    贾树晋, 杜斌, 岳恒. 基于局部搜索与混合多样性策略的多目标粒子群算法[J]. 控制与决策, 2012, 27(6): 813-818.

    Jia S J, Du B, Yue H. Local search and hybrid diversity strategy based multi-objective particle swarm optimization algorithm[J]. Control and Decision, 2012, 27(6): 813-818.
    [15]
    彭晓波, 桂卫华, 黄志武, 等. GAPSO: 一种高效的遗传粒子混合算法及其应用[J]. 系统仿真学报, 2008, 20(18): 5025-5027.

    Peng X B, Gui W H, Huang Z W, et al. GAPSO: Effective genetic particle swarm algorithm and its application[J]. Journal of System Simulation, 2008, 20(18): 5025-5027.
    [16]
    栾丽君, 谭立静, 牛奔. 一种基于粒子群优化算法和差分进化算法的新型混合全局优化算法[J]. 信息与控制, 2007, 36(6): 708-714.

    Luan L J, Tan L J, Niu B. A novel hybrid global optimization algorithm based on particle swarm optimization and differential evolution[J]. Information and Control, 2007, 36(6): 708-714.
    [17]
    宋晓宇, 王建国, 常春光. 基于需求紧迫度的非线性连续消耗应急调度模型与算法[J]. 信息与控制, 2014, 43(6): 735-743.

    Song X Y, Wang J G, Chang C G. Nonlinear continuous consumption emergency material dispatching model based on demand urgency degrees and its algorithm[J]. Information and Control, 2014, 43(6): 735-743.
    [18]
    简平. 邹鹏. 熊伟. 基于DPSO-SA的低轨预警系统初始任务规划方法[J]. 北京航空航天大学学报, 2013, 39(10): 1381-1386.

    Jian P, Zou P, Xiong W. Original task planning method of early warning system of LEO based on DPSO-SA[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(10): 1381-1386.
    [19]
    汤绍勋, 易先清, 罗雪山. 面向预警卫星调度问题的改进粒子群算法[J]. 系统工程, 2012, 30(1): 116-121.

    Tang S X, Yi X Q, Luo X S. An improved particle swarm optimization algorithm for early warning satellites scheduling problems[J]. Systems Engineering, 2012, 30(1): 116-121.
    [20]
    张晓缋, 方浩, 戴冠中. 遗传算法的编码机制研究[J]. 信息与控制, 1997, 26(2): 55-60.

    Zhang X H, Fang H, Dai G Z. Studies on encoding mechanism of genetic algorithms[J]. Information and Control, 1997, 26(2): 55-60.
  • Related Articles

    [1]WANG Rui, XU Xinchao, LU Jing. Short-term Wind Power Prediction Based on SSA Optimized Variational Mode Decomposition and Hybrid Kernel Extreme Learning Machine[J]. INFORMATION AND CONTROL, 2023, 52(4): 444-454. DOI: 10.13976/j.cnki.xk.2023.2281
    [2]KONG Wen, CHE Quan, ZHAO Huirong, PENG Daogang. Short-term Prediction of Coal Stock in Power Plant Based on Singular Spectrum Analysis and Long Short-term Memory Neural Network[J]. INFORMATION AND CONTROL, 2020, 49(6): 742-751. DOI: 10.13976/j.cnki.xk.2020.9484
    [3]ZHAO Chao, DAI Kuncheng. Power System Short-term Load Forecasting Based on Adaptive Weighted Least Squares Support Vector Machine[J]. INFORMATION AND CONTROL, 2015, 44(5): 634-640. DOI: 10.13976/j.cnki.xk.2015.0634
    [4]XU Qin, TANG Xinmin, HAN Songchen, LU Yiyu. Short-Term 4D Trajectory Prediction Based on Parameter Identification[J]. INFORMATION AND CONTROL, 2014, 43(4): 501-505,512. DOI: 10.13976/j.cnki.xk.2014.0501
    [5]WANG Tong, GAO Xianwen, ZHAI Yujia, LIU Chunfang. Improved Detection and Isolation of Sensor Fault Based on PSO-LSSVM Prediction[J]. INFORMATION AND CONTROL, 2014, 43(2): 146-151. DOI: 10.3724/SP.J.1219.2014.00146
    [6]CAO Kai, PENG Dongdong, CHEN Feng, LI Xiuhai. A modeling Approach Based on Combined Short-term and Long-term Prediction[J]. INFORMATION AND CONTROL, 2013, 42(4): 430-436. DOI: 10.3724/SP.J.1219.2013.00430
    [7]YAN Gang, LIANG Ximing, LONG Zuqiang, LONG Weng. Double Mode Control Based on Least Squares Support Vector Machine[J]. INFORMATION AND CONTROL, 2011, 40(6): 721-727.
    [8]ZHANG Xiaoping, TANG Zhenxing, ZHAO Jun, WANG Wei, CONG Liqun, FENG Weimin. Online Prediction Modelling of Blast Furnace Gas Output in Steel Industry[J]. INFORMATION AND CONTROL, 2010, 39(6): 774-782.
    [9]LIANG Ximing, YAN Gang, LI Shanchun, LONG Wen, LONG Zuqiang. Nonlinear Predictive Control Based on Least Squares Support Vector Machines and Chaos Optimization[J]. INFORMATION AND CONTROL, 2010, 39(2): 129-135.
    [10]YANG Yan-xi, LIU Ding, LI Qi, ZHENG Gang. SHORT TERM LOAD FORECASTING USING A MULTILAYER NEURAL NETWORK WITH BP-GA MIXED ALGORITHMS[J]. INFORMATION AND CONTROL, 2002, 31(3): 284-288.

Catalog

    Article views (738) PDF downloads (307) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return