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
  • [31] An improved layered parallel particle swarm optimisation algorithm for the interchange traffic control
    He, Ruichun
    Ma, Changxi
    INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2015, 6 (05) : 434 - 441
  • [32] Research on Coverage algorithm for Wireless Sensor Networks based on improved particle swarm optimization algorithm
    Yin, Xiaoqi
    Guo, Yizhuo
    Li, Xiaofeng
    Wang, Xuemei
    2017 INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS, ELECTRONICS AND CONTROL (ICCSEC), 2017, : 1207 - 1210
  • [33] An improved particle swarm optimiser based on swarm success rate for global optimisation problems
    Adewumi, Aderemi Oluyinka
    Arasomwan, Akugbe Martins
    JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2016, 28 (03) : 441 - 483
  • [34] An improved multi-leader comprehensive learning particle swarm optimisation based on gravitational search algorithm
    Amponsah, Alfred Adutwum
    Han, Fei
    Osei-Kwakye, Jeremiah
    Bonah, Ernest
    Ling, Qing-Hua
    CONNECTION SCIENCE, 2021, 33 (04) : 803 - 834
  • [35] Optimizing Equipment Effectiveness Based on Particle Swarm Optimization Algorithm
    Li, Dong
    Kang, Wen-zheng
    Ma, Hai-yang
    ASIA-PACIFIC YOUTH CONFERENCE ON COMMUNICATION TECHNOLOGY 2010 (APYCCT 2010), 2010, : 475 - +
  • [36] Evaluation mode research on particle swarm optimization algorithm
    Kang Qi
    Wang Lei
    Xiao Hui
    Wu Qidi
    2007 IEEE INTERNATIONAL CONFERENCE ON NETWORKING, SENSING, AND CONTROL, VOLS 1 AND 2, 2007, : 846 - +
  • [37] An improved two-swarm based particle swarm optimization algorithm
    Li, Ting
    Lai, Xuzhi
    Wu, Min
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 3129 - +
  • [38] Research on Improved Particle Swarm Optimization Algorithm in WSN Routing
    Cheng, Xiaohui
    Tong, Huihui
    2019 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION PROCESSING (ICIIP 2019), 2019, : 451 - 454
  • [39] An Adaptive Particle Swarm Optimization Algorithm Based on Aggregation Degree
    Zhang, Xiuli
    Zhang, Ruihua
    Wang, Jianping
    Wang, Laidi
    RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING, 2018, 11 (04) : 443 - 448
  • [40] Improved VRP based on particle swarm optimization algorithm
    Chen, Zixia
    Xuan, Youshi
    DCABES 2006 PROCEEDINGS, VOLS 1 AND 2, 2006, : 436 - 439