应用于物联网的现代信息智能处理技术
Modern Information Intelligent Processing Technology Applied to the Internet of Things
DOI: 10.12677/ORF.2023.136744, PDF,   
作者: 许钰雪, 刘 笑：上海工程技术大学管理学院，上海
关键词: 物联网；蜂群算法；智能计算；Internet of Things； Bee Swarm Algorithm； Intelligent Computing
摘要: 现代智能信息处理方法是指模仿生物智能利用计算机及人工智能技术和计算机科学处理信息。本文基于梳理现代智能信息处理的发展历程、结构特性，以物联网行业为案例探讨智能计算在物联网的应用。另外，也对智能信息处理的发展与应用进行了展望，详细描述了蜂群算法(ABC)特征的系统结构，为解决物联网在大数据特征算法选择方面低计算效率、低可扩展性的问题提供理论基础，此架构由四个层次组成，能够高效地收集有效数据，清除不需要的数据，具有更好的扩展性和有效性。
Abstract: Modern intelligent information processing method refers to the imitation of biological intelligence using computer and artificial intelligence technology and computer science to process information. Based on the development history and structural characteristics of modern intelligent information processing, this paper discusses the application of intelligent computing in the Internet of Things by taking the Internet of Things industry as a case study. In addition, it also prospects the development and application of intelligent information processing, and describes in detail the system structure of bee swarm algorithm (ABC) features, which provides a theoretical basis for solving the problems of low computational efficiency and low scalability in the selection of big data feature algorithms in the Internet of Things. This architecture consists of four levels, which can efficiently collect effective data and remove unwanted data. It has better scalability and effectiveness.
文章引用：许钰雪, 刘笑. 应用于物联网的现代信息智能处理技术[J]. 运筹与模糊学, 2023, 13(6): 7590-7597. https://doi.org/10.12677/ORF.2023.136744

参考文献

[1] Karaboga, D. (2005) An Idea Based on Honey Bee Swarm for Numerical Optimization, TR06. Erciyes University, Kayseri.
[2] Karaboga, D., Gorkemli, B., Ozturk, C. and Karaboga, N. (2014) A Comprehensive Survey: Artificial Bee Colony (ABC) Algorithm and Applications. Artificial Intelligence Review, 42, 21-57. [Google Scholar] [CrossRef] 
[3] 秦全德, 程适, 李丽, 等. 人工蜂群算法研究综述[J]. 智能系统学报, 2014, 9(2): 127-135.
[4] 霍凤财, 杜颖, 刘洋. 人工蜂群算法及其应用[J]. 吉林大学学报(信息科学版), 2016, 34(4): 468-476.
[5] Karaboga, D. and Akay, B. (2009) A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation, 214, 108-132. [Google Scholar] [CrossRef] 
[6] Karaboga, D. and Basturk, B. (2008) On the Performance of Artificial Bee Colony (ABC) Algorithm. Applied Soft Computing, 8, 687-697. [Google Scholar] [CrossRef] 
[7] Akay, B. and Karaboga, D. (2009) Parameter Tuning for the Artificial Bee Colony Algorithm. In: Nguyen, N.T., Kowalczyk, R. and Chen, S.M., Eds., Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, Springer, Berlin, 608-619. [Google Scholar] [CrossRef] 
[8] 宁爱平, 张雪英. 人工蜂群算法的收敛性分析[J]. 控制与决策, 2013, 28(10): 1554-1558.
[9] Karaboga, D. and Basturk, B. (2007) A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization, 39, 459-471. [Google Scholar] [CrossRef] 
[10] Krishnanand, K.R., Nayak, S.K., Panigrahi, B.K. and Rout, P.K. (2009) Comparative Study of Five Bio-Inspired Evolutionary Optimization Techniques. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, 9-11 December 2009, 1231-1236. [Google Scholar] [CrossRef] 
[11] Mala, D.J., Kamalapriya, M., Shobana, R. and Mohan, V. (2009) A Non-Pheromone Based Intelligent Swarm Optimization Technique in Software Test Suite Optimization. 2009 International Conference on Intelligent Agent & Multi-Agent Systems, Chennai, 22-24 July 2009, 1-5. [Google Scholar] [CrossRef] 
[12] Karaboga, D. and Akay, B. (2009) Artificial Bee Colony (ABC), Harmony Search and Bees Algorithms on Numerical Optimization. Proceeding of Innovative Production Machines and Systems Virtual Conference 2009, Kayseri, January 2009, 1-6.
[13] Li, H.Z., Liu, K.Q. and Li, X. (2010) A Comparative Study of Artificial Bee Colony, Bees Algorithms and Differential Evolution on Numerical Benchmark Problems. In: Cai, Z., Tong, H., Kang, Z. and Liu, Y., Eds., Computational Intelligence and Intelligent Systems, Springer, Berlin, 198-207. [Google Scholar] [CrossRef] 
[14] Chu, S.C., Huang, H.C., Roddick, J.F. and Pan, J.S. (2011) Overview of Algorithms for Swarm Intelligence. In: Jędrzejowicz, P., Nguyen, N.T. and Hoang, K., Eds., Computational Collective Intelligence. Technologies and Applications, Springer, Berlin, 28-41. [Google Scholar] [CrossRef] 
[15] Civicioglu, P. and Besdok, E. (2013) A Conceptual Comparison of the Cuckoosearch, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms. Artificial Intelligence Review, 39, 315-346. [Google Scholar] [CrossRef] 
[16] 邱云飞, 李智义. 改进人工鱼群算法在SVM参数优化中的应用[J]. 计算机工程与科学, 2018, 40(11): 2074-2079.
[17] 苏占东, 杨炳濡, 游福成. 基于信息挖掘的智能决策支持系统的结构设计[J]. 计算机应用研究, 2012, 20(5): 12-14.
[18] 黄莉. 智能信息与网络处理探究[J]. 电脑知识与技术, 2010, 6(18): 5028, 5035.
[19] 童晓渝, 房秉毅, 张云勇. 物联网智能家居发展分析[J]. 移动通信, 2010, 34(9): 16-20.
[20] 杨堤. 基于物联网的智能家居控制系统设计与实现[J]. 电子世界, 2012(21): 16-17.
[21] 王怡, 鄂旭. 基于物联网无线传感的智能家居研究[J]. 计算机技术与发展, 2015(2): 234-237.
[22] 由斌, 张桂青, 汪明, 李成栋. 基于物联网的智能家居设计与实现[J]. 自动化与仪表, 2013, 28(2): 6-10.
[23] Bonabeau, E., Dorigo, M. and Theraulaz, G. (1999) Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York. [Google Scholar] [CrossRef] 
[24] 黄席樾. 现代智能信息算法理论及应用[M]. 北京: 科学出版社, 2009.
[25] 陈国良. 遗传算法及其应用[M]. 北京: 人民邮电出版社, 1996.
[26] 林明星, 付晨. 基于神经网络的多传感器信息融合技术[J]. 新技术新工艺, 1999(3): 6.
[27] 王雪, 王晟. 现代智能信息处理实践方法[M]. 北京: 清华大学出版社, 2009.
[28] 王天力. 人工蜂群算法 + BP神经网络模型在短期电力负荷预测中的应用研究[J]. 机电信息, 2022(3): 6-9.
[29] 王玉, 申铉京, 周昱洲, 林鸿斌. 一种求解交通网络中最短路径问题的人工蜂群算法[J]. 吉林大学学报(理学版), 2021, 59(5): 1144-1150.
[30] 单好民, 陈才学. 基于RSSI高斯滤波的人工蜂群定位算法[J]. 传感技术学报, 2021, 34(7): 979-983.
[31] 黄英双, 曹辉. 改进人工蜂群算法优化支持向量机及应用[J]. 计算机应用与软件, 2021, 38(2): 258-263, 277.
[32] 李昊伦. 改进人工蜂群算法及相关应用研究[D]: [硕士学位论文]. 南京: 南京邮电大学, 2020.
[33] 徐晓飞, 刘志中, 王忠杰, 闵寻优, 刘睿霖, 王海芳. S-ABC——面向服务领域的人工蜂群算法范型[J]. 计算机学报, 2015, 38(11): 2301-2317.
[34] 耿立卓, 郝雪, 刘璐, 林春龙, 高琦. 基于人工蜂群算法的电力通信网路由优化仿真[J]. 电子设计工程, 2022, 30(24): 28-32.
[35] 许犇. 面向工业智能的制造流程优化方法研究[D]: [硕士学位论文]. 南京: 南京邮电大学, 2022.
[36] 杨帆, 李邵梅, 金柯君. 基于改进人工蜂群算法的文本对抗样本生成[J]. 计算机系统应用, 2022, 31(11): 238-245.

为你推荐