Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (12): 965-975    DOI: 10.1631/jzus.C1100045
    
A hybrid genetic algorithm to optimize device allocation in industrial Ethernet networks with real-time constraints
Lei Zhang1, Mattias Lampe2, Zhi Wang*,1
1 State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China 2 Corporate Technology, Siemens Ltd., Beijing 100102, China
A hybrid genetic algorithm to optimize device allocation in industrial Ethernet networks with real-time constraints
Lei Zhang1, Mattias Lampe2, Zhi Wang*,1
1 State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China 2 Corporate Technology, Siemens Ltd., Beijing 100102, China
 全文: PDF(721 KB)  
摘要: With the advance of automation technology, the scale of industrial communication networks at field level is growing. Guaranteeing real-time performance of these networks is therefore becoming an increasingly difficult task. This paper addresses the optimization of device allocation in industrial Ethernet networks with real-time constraints (DAIEN-RC). Considering the inherent diversity of real-time requirements of typical industrial applications, a novel optimization criterion based on relative delay is proposed. A hybrid genetic algorithm incorporating a reduced variable neighborhood search (GA-rVNS) is developed for DAIEN-RC. Experimental results show that the proposed novel scheme achieves a superior performance compared to existing schemes, especially for large scale industrial networks.
关键词: OptimizationReal-timeIndustrial EthernetDevice allocationSteady-state genetic algorithmVariable neighborhood search    
Abstract: With the advance of automation technology, the scale of industrial communication networks at field level is growing. Guaranteeing real-time performance of these networks is therefore becoming an increasingly difficult task. This paper addresses the optimization of device allocation in industrial Ethernet networks with real-time constraints (DAIEN-RC). Considering the inherent diversity of real-time requirements of typical industrial applications, a novel optimization criterion based on relative delay is proposed. A hybrid genetic algorithm incorporating a reduced variable neighborhood search (GA-rVNS) is developed for DAIEN-RC. Experimental results show that the proposed novel scheme achieves a superior performance compared to existing schemes, especially for large scale industrial networks.
Key words: Optimization    Real-time    Industrial Ethernet    Device allocation    Steady-state genetic algorithm    Variable neighborhood search
收稿日期: 2011-02-28 出版日期: 2011-11-30
CLC:  TP393.11  
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
Lei Zhang
Mattias Lampe
Zhi Wang

引用本文:

Lei Zhang, Mattias Lampe, Zhi Wang. A hybrid genetic algorithm to optimize device allocation in industrial Ethernet networks with real-time constraints. Front. Inform. Technol. Electron. Eng., 2011, 12(12): 965-975.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100045        http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I12/965

[1] T T DHIVYAPRABHA, P SUBASHINI, M KRISHNAVENI. Synergistic fibroblast optimization: a novel nature-inspired computing algorithm[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(7): 815-833.
[2] Qiang LAN, Lin-bo QIAO, Yi-jie WANG. Stochastic extra-gradient based alternating direction methods for graph-guided regularized minimization[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(6): 755-762.
[3] Lai TENG, Zhong-he JIN. A composite optimization method for separation parameters of large-eccentricity pico-satellites[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(5): 685-698.
[4] Muhammad KAMRAN, Ehsan Ullah MUNIR. On the role of optimization algorithms in ownership-preserving data mining[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(2): 151-164.
[5] Li XIE, Yi-qun ZHANG, Jun-yan XU. Hohmann transfer via constrained optimization[J]. Front. Inform. Technol. Electron. Eng., 2018, 19(11): 1444-1458.
[6] Xing-chen WU , Gui-he QIN , Ming-hui SUN , He YU , Qian-yi XU. Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(9): 1385-1395.
[7] Lin CAO , Shuo TANG , Dong ZHANG. Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(7): 882-897.
[8] Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI. Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(12): 1991-2000.
[9] Feng LIU, Dan ZENG, Jing LI, Qi-jun ZHAO. On 3D face reconstruction via cascaded regression in shape space[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(12): 1978-1990.
[10] Chao GUO, Zeng-xuan HOU, You-zhi SHI, Jun XU, Dan-dan YU . A virtual 3D interactive painting method for Chinese calligraphy and painting based on real-time force feedback technology[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(11): 1843-1854.
[11] Mian CHENG, Jin-shu SU, Jing XU. Real-time pre-processing system with hardware accelerator for mobile core networks[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(11): 1720-1719.
[12] Xiao-qing ZHANG, Zheng-feng MING. An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(11): 1705-1719.
[13] Meng LI, Xi LIN, Xi-qun CHEN. A surrogate-based optimization algorithm for network design problems[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(11): 1693-1704.
[14] Lan HUANG, Gui-chao WANG, Tian BAI, Zhe WANG. An improved fruit fly optimization algorithm for solving traveling salesman problem[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(10): 1525-1533.
[15] . Image meshing via hierarchical optimization[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(1): 32-40.