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

被引：18
|
作者：
Wang, Rong [1 ]
Feng, Yue [1 ]
机构：
[1] Nanjing Inst Technol, Sch Econ & Management, Nanjing 211167, Jiangsu, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2020年 / 131卷 / 131期
关键词：
Green degree; Equipment manufacturing industry; Sustainable development; ECO-INNOVATION;
D O I：
10.1016/j.chaos.2019.109502
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The paper constructs the green degree evaluation system of equipment manufacturing industry, and then the improved particle swarm optimization algorithm is used to measure the green degree of China's equipment manufacturing industry. The results show that the improved particle swarm optimization algorithm is a better method to evaluate the green degree of China's equipment manufacturing industry. The paper measure the green degree of China's equipment manufacturing industry is 0.51, which is at a low level. So it is need to adopt policies to promote green degree to achieve sustainable development goals. In view of this, the paper proposes policy recommendations for the green degree of China's equipment manufacturing industry from the perspectives of improving relevant policies, technological upgrading and environmental protection input, applying legal policy systems, and increasing financial support. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数：6
相关论文
共 50 条
  • [1] Evaluation research on green degree of equipment manufacturing industry based on improved particle swarm optimisation algorithm
    Li Z.
    Li, Zhang (66082036@qq.com), 1600, Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (12): : 217 - 221
  • [2] Research on evaluation algorithm of enterprise informatization maturity based on improved particle swarm optimization algorithm
    Zhu, Jianbin
    Journal of Chemical and Pharmaceutical Research, 2014, 6 (07) : 2560 - 2565
  • [3] Research of improved particle swarm optimization algorithm
    Ding, Zhiping
    MATERIALS SCIENCE, ENERGY TECHNOLOGY, AND POWER ENGINEERING I, 2017, 1839
  • [4] Research on fast clustering algorithm based on improved particle swarm optimization
    Sheng Hai-long
    2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), 2014, : 798 - 802
  • [5] Research of improved particle swarm optimization algorithm based on big data
    Wang, Yanmin
    2019 INTERNATIONAL CONFERENCE ON ROBOTS & INTELLIGENT SYSTEM (ICRIS 2019), 2019, : 287 - 290
  • [6] 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
  • [7] Research on Micro/Nano Surface Flatness Evaluation Method Based on Improved Particle Swarm Optimization Algorithm
    Shu, Han
    Zou, Chunlong
    Chen, Jianyu
    Wang, Shenghuai
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2021, 9
  • [8] 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,
  • [9] 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
  • [10] 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):
12345