Evaluation research on green degree of equipment manufacturing industry based on improved particle swarm optimisation algorithm

被引：0
|
作者：
Li Z. [1 ]
机构：
[1] School of Electronic Information Engineering, Rizhao Polytechnic, Rizhao, Shandong
来源：
Li, Zhang (66082036@qq.com) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Equipment manufacturing industry; Green degree; Improved particle swarm algorithm;
D O I：
10.1504/ijris.2020.109643
中图分类号：
学科分类号：
摘要：
In order to improve the sustainable development of equipment manufacturing industry, the improved particle swarm algorithm is applied in evaluating green degree of equipment manufacturing industry. Firstly, the green degree evaluation system of equipment manufacturing industry is constructed, and evaluation index system is established. Secondly, the basic theory of particle swarm algorithm and the improved particle swarm algorithm are studied basing on analysis of disadvantages of traditional particle swarm algorithm. Thirdly, the analysis procedure of improved particle swarm algorithm is designed. Finally, equipment manufacturing industry in a province is used as a researching object, the green degree evaluation of equipment manufacturing industry in this province is carried out, and results show that this algorithm can improve evaluation level of green degree of equipment manufacturing industry. © 2020 Inderscience Enterprises Ltd.
引用
收藏
页码：217 / 221
页数：4
相关论文
共 50 条
  • [21] A novel improved hybrid particle swarm optimisation based genetic algorithm for the solution to layout problems
    Zhao, Fengqiang
    Li, Guangqiang
    Hu, Hongying
    Du, Jialu
    Guo, Chen
    Li, Tao
    2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 5041 - 5046
  • [22] Research on Target Localization based on Improved Multi-swarm Particle Swarm Optimization Algorithm
    Yao, Jinjie
    Han, Yan
    2010 6TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS NETWORKING AND MOBILE COMPUTING (WICOM), 2010,
  • [23] An Algorithm Based on the Improved Particle Swarm Optimization
    Ge, Ri-Bo
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, KNOWLEDGE ENGINEERING AND INFORMATION ENGINEERING (SEKEIE 2014), 2014, 114 : 176 - 179
  • [24] The research of particle swarm algorithm based on heuristic rules for the layout of airplane equipment cabin
    Han, Xiaojian
    Ding, Xiangfang
    Xiao, Chun
    MECHATRONICS AND APPLIED MECHANICS II, PTS 1 AND 2, 2013, 300-301 : 659 - 663
  • [25] Equipment Manufacturing Industry Knowledge Chain Efficiency Prediction Algorithm Based on Improved RBFNN
    Chen Xu Sheng
    Xu Chen Peng
    Wang Hong Qi
    MACHINERY ELECTRONICS AND CONTROL ENGINEERING III, 2014, 441 : 776 - 779
  • [26] Microgrid Economic Operation Research Based on Improved Particle Swarm Optimization Algorithm
    Wang, Xueying
    Li, Peng
    PROCEEDINGS OF THE 2015 3RD INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND INFORMATION TECHNOLOGY APPLICATIONS, 2015, 35 : 290 - 294
  • [27] Research of BP neural network based on improved particle swarm optimization algorithm
    School of Mechanical and Information Engineering, China University of Mining and Technology, Beijing, China
    不详
    不详
    J. Netw., 2013, 4 (947-954):
  • [28] Research on charging strategy based on improved particle swarm optimization PID algorithm
    Wang, Xiuzhuo
    Tang, Yanfeng
    Li, Zeyao
    Xu, Chunsheng
    COMPLEX & INTELLIGENT SYSTEMS, 2024, 10 (05) : 6421 - 6433
  • [29] Research on classification of privacy protection based on Improved Particle Swarm Optimization Algorithm
    Chen Yu
    Tang Yuanxin
    Zhou Zhou
    FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE IV, PTS 1-5, 2014, 496-500 : 1861 - 1864
  • [30] Accounting Data Quality Evaluation Method Based on Improved Particle Swarm Algorithm
    Sun, Mengying
    ADVANCES IN MULTIMEDIA, 2022, 2022
12345