Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN:
Online ISSN:


  About PCA
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)

 

 
Progress in Computing Applications(PCA)
 

Environmental Governance and Protection Financing Scheme Based on a Dimensional Multi-Objective Genetic Algorithm
Fangying Yuan, Xue Li, Fei Peng
Asia Pacific University of Technology &Innovation, Malaysia
Abstract: This article proposes an environmental governance and protection financing scheme based on a low-dimensional multi-objective genetic algorithm to solve the problem of insufficient funding for environmental governance and protection projects. This plan effectively utilises funds, improves investment returns, and reduces risks by optimizing portfolio investments. This article first introduces the background and significance of environmental governance and protection financing, then elaborates on applying a low dimensional multi-objective genetic algorithm in optimizing portfolio investment. Finally, the feasibility and effectiveness of this scheme are verified through experiments. The experimental results indicate that the environmental governance and protection financing scheme based on a low dimensional multi-objective genetic algorithm can effectively solve the financing problem of environmental governance and protection projects and has important theoretical significance and practical application value.
Keywords: Environmental Pollution Control, Environmental Protection, Investment and Financing, Strategy Research Environmental Governance and Protection Financing Scheme Based on a Dimensional Multi-Objective Genetic Algorithm
DOI:https://doi.org/10.6025/pca/2023/12/2/35-44
Full_Text   PDF 1 MB   Download:   62  times
References:

[1] Cerrada, M., Zurita, G., Cabrera, D, et al. (2016). Fault diagnosis in spur gears based on genetic algorithm and random forest. Mechanical Systems & Signal Processing, p 70–71, 87-103.
[2] Metawa, N., Hassan, M. K., Elhoseny, M. (2017). Genetic algorithm-based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 75-82.
[3] Gai, K, Qiu, M, Zhao, H. (2016). Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing. IEEE Transactions on Cloud Computing, (99), 1-1.
[4] Costa, A., Cappadonna, F A., Fichera, S. (2017). A hybrid genetic algorithm for minimizing makespan in a flow-shop sequencedependent group scheduling problem. Journal of Intelligent Manufacturing, 28 (6), 1-15.
[5] Yang, M D,, Chen Y P., Lin, Y H., et al. (2016). Multiobjective optimization using nondominated sorting genetic algorithm-II for allocation of energy conservation and renewable energy facilities in a campus. Energy & Buildings, 122, 120-130.
[6] Weuster-Botz, D., Wandrey, C. (2016). Medium Optimization by Genetic Algorithm for Continuous Production of Formate Dehydrogenase. Journal of Dali University, 30 (6), 563-571.
[7] Li, C L., Chen, S H., Yang, C M., et al. (2016). Image reconstruction for a partially immersed perfectly conducting cylinder using the steady state genetic algorithm. Radio Science, 39 (2), 1-10.
[8] Li, X., Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. International Journal of Production Economics, 174, 93-110.
[9] Kalayci, C B., Polat, O., Gupta, S M. (2016). A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem. Annals of Operations Research, 242 (2), 321-354.
[10] Bos, M, Weber, H T. (2017). Comparison of the training of neural networks for quantitative x-ray fluorescence spectrometry by a genetic algorithm and backward error propagation. Analytica Chimica Acta, 247 (1), 97-105.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info