An optimisation of higher education resources search method based on multi-state hierarchical model

被引:0
|
作者
Li, Ping [1 ]
机构
[1] Qingdao Vocat & Tech Coll Hotel Management, Dept Basic Educ, Qingdao 266100, Peoples R China
关键词
multi-state hierarchical model; higher education resources; search method optimisation; ant colony algorithm; heterogeneous multiple ant colony algorithm; ALGORITHM;
D O I
10.1504/IJCEELL.2024.136993
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
In order to overcome the problems of low recall rate, precision rate and large search time consumption of traditional methods, optimisation of higher education resources search method based on multi-state hierarchical model is studied. This paper analyses the objective function of higher education resources search, sets the related constraint conditions, and selects the higher education resources search mode by ant colony algorithm. In order to further improve the search quality, the ant colony algorithm was improved by selecting ant colony species, determining communication subgroups, communication period and information exchange between subgroups. The algorithm was used to optimise the search mode, that is, resource stratified search. A multi-state hierarchical model is built to search higher education resources. Experimental results show that the recall rate of this method is always above 93%, the precision rate is above 92%, and the average search time consumption is 0.66 s.
引用
收藏
页码:179 / 193
页数:16
相关论文
共 50 条
  • [21] On Dependent Multi-State Semi-Coherent Systems Based on Multi-State Joint Signature
    He Yi
    Narayanaswamy Balakrishnan
    Lirong Cui
    Methodology and Computing in Applied Probability, 2022, 24 : 1717 - 1734
  • [23] The multi-state CASPT2 method
    Finley, J
    Malmqvist, PA
    Roos, BO
    Serrano-Andres, L
    CHEMICAL PHYSICS LETTERS, 1998, 288 (2-4) : 299 - 306
  • [24] A multi-state model updating method for structures in high-temperature environments
    Yuan, Zhaoxu
    Yu, Kaiping
    Bai, Yunhe
    MEASUREMENT, 2018, 121 : 317 - 326
  • [25] A Dynamic Model of Multi-state LVAD Based on LSTM Neural Network
    Tan, Aiping
    Mu, Ying
    Yu, Wenqian
    Liang, Chenxi
    Chen, Yanfeng
    ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PT V, ICIC 2024, 2024, 14879 : 203 - 214
  • [26] The impact of completing upper secondary education - a multi-state model for work, education and health in young men
    Hoff, Rune
    Corbett, Karina
    Mehlum, Ingrid S.
    Mohn, Ferdinand A.
    Kristensen, Petter
    Hanvold, Therese N.
    Gran, Jon M.
    BMC PUBLIC HEALTH, 2018, 18
  • [27] A multi-state model for kidney disease progression
    Lintu, M. K.
    Shreyas, K. M.
    Kamath, Asha
    CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH, 2022, 13
  • [28] A MULTI-STATE MODEL OF METATSTATIC COLORECTAL CANCER
    van Rooijen, E. M.
    Coupe, V. M. H.
    Koopman, M.
    Punt, C. J. A.
    Uyl-De Groot, C. A.
    VALUE IN HEALTH, 2014, 17 (07) : A630 - A630
  • [29] The impact of completing upper secondary education - a multi-state model for work, education and health in young men
    Rune Hoff
    Karina Corbett
    Ingrid S. Mehlum
    Ferdinand A. Mohn
    Petter Kristensen
    Therese N. Hanvold
    Jon M. Gran
    BMC Public Health, 18
  • [30] Integrated importance of multi-state fault tree based on multi-state multi-valued decision diagram
    Li, Shumin
    Si, Shubin
    Xing, Liudong
    Sun, Shudong
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY, 2014, 228 (02) : 200 - 208