Application research on improved particle swarm computational intelligence algorithm for multi-objective optimization in ideological and political education

被引:0
|
作者
Sun, Lingxiu [1 ]
Rui, Mao [2 ]
机构
[1] Wuchang Inst Technol, Dept Econ & Management, Wuhan, Hubei, Peoples R China
[2] Wuchang Inst Technol, Sch Econ & Management, Wuhan, Hubei, Peoples R China
关键词
Ideological and political education; online teaching; particle swarm computation; multi-objective optimization;
D O I
10.3233/JCM-230024
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In today's world, optimization problems are becoming increasingly prominent, and the progress in optimization technologies can not only bring considerable economic benefits but also highlight their outstanding social value, making significant contributions to the sustainable development of the ecological environment. Due to their educational positioning and disciplinary development needs, local application-oriented universities overlook the optimization and development of ideological and political theory courses in their growth, leading to lagging reforms in ideological and political education and suboptimal teaching outcomes. To enhance the teaching effects of ideological and political courses in local application-oriented universities, it is essential to scientifically design class contents, actively carry out practical teaching, adapt to the needs of the times, build an "Internet + Ideological and Political Courses" online teaching platform, and continuously innovate teaching modes of ideological and political courses.
引用
收藏
页码:2061 / 2067
页数:7
相关论文
共 50 条
  • [1] Research on Improved Particle Swarm Computational Intelligence Algorithm and Its Application to Multi-Objective Optimisation
    Chen L.
    Xiong F.
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [2] Improved multi-objective particle swarm optimization algorithm
    College of Automation, Northwestern Polytechnical University, Xi'an 710129, China
    不详
    Liu, B. (lbn1987113@163.com), 2013, Beijing University of Aeronautics and Astronautics (BUAA) (39):
  • [3] An improved multi-objective particle swarm optimization algorithm
    Zhang, Qiuming
    Xue, Siqing
    ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2007, 4683 : 372 - +
  • [4] Application of improved particle swarm optimization algorithm to multi-objective reactive power optimization
    Li, Xinbin
    Zhu, Qingjun
    Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, 2010, 25 (07): : 137 - 143
  • [5] Application of improved multi-objective particle swarm optimization algorithm in discrete combinatorial optimization
    Xia, Yu
    Wu, Peng
    Wu, Tianshu
    Chu, Da
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 156 - 156
  • [6] An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm
    Zhou, Zuan
    Dai, Guangming
    Fang, Pan
    Chen, Fangjie
    Tan, Yi
    ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2008, 5370 : 181 - 188
  • [7] IMOPSO: An Improved Multi-objective Particle Swarm Optimization Algorithm
    Ma, Borong
    Hua, Jun
    Ma, Zhixin
    Li, Xianbo
    PROCEEDINGS OF 2016 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), 2016, : 376 - 380
  • [8] Research on improved multi-objective particle swarm optimization algorithms
    Zhao, Duo
    Jin, Weidong
    APPLIED ARTIFICIAL INTELLIGENCE, 2006, : 231 - +
  • [9] Application and optimization design of improved multi-objective particle swarm
    Zhang, Lan-Yong
    Liu, Sheng
    Yu, Da-Yong
    Dianbo Kexue Xuebao/Chinese Journal of Radio Science, 2011, 26 (04): : 789 - 795
  • [10] The Research of Parallel Multi-objective Particle Swarm Optimization Algorithm
    Wu Jian
    Tang XinHua
    Cao Yong
    2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2014, : 300 - 304