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A Decision Model for Public Economic Environment Monitoring and Management Decision Using Improved Ant Colony Algorithm

The quality of economic development is not sufficient, despite the apparent role that public environmental monitoring and management play in the economy's rapid expansion. Therefore, enhancing the standard of economic growth is of the utmost importance. It is vital to establish innovative manag...

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Detalles Bibliográficos
Autores principales: Ji, Hongmei, Li, Zhuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534691/
https://www.ncbi.nlm.nih.gov/pubmed/36213027
http://dx.doi.org/10.1155/2022/4096566
Descripción
Sumario:The quality of economic development is not sufficient, despite the apparent role that public environmental monitoring and management play in the economy's rapid expansion. Therefore, enhancing the standard of economic growth is of the utmost importance. It is vital to establish innovative management techniques to support social and governmental transformation in order to raise the standard of economic development. Economic development affects public administration's fundamental duties and goals. The division of public administration distributes social resources and enhances the market's function. The fundamental problem in public economic management is how to successfully actualize rational resource allocation, advance social justice, and boost social welfare on the basis of resource scarcity and the breakdown of the market mechanism. This study greatly increases the global optimization performance of the fundamental ant colony method and provides the explicit programme and simulation stages for the new algorithm. Finally, simulation tests are conducted using the basic ant colony algorithm and the upgraded ant colony algorithm in relation to the decision-making model of public economic management. According to the simulation findings, the revised algorithm successfully addresses the traditional approach's drawbacks, including its slow convergence speed and propensity to easily enter local minima, and its optimization performance has increased by 30.23%.