Dynamic programming method of sustainable land use based on multi-objective genetic algorithm

Wuyi Zhou

International Journal of Environmental Technology and Management, 2024, vol. 27, issue 4/5/6, 356-369

Abstract: The traditional dynamic programming method of sustainable land use has some problems, such as low ecological benefit coefficient and large error in calculating the economic value of planning results. Therefore, a dynamic programming method of sustainable land use based on multi-objective genetic algorithm is proposed. Firstly, the population discrete model is used to deeply predict the per capita land use demand and determine the multi-objective object of dynamic programming. Secondly, using the grey objective programming method, the planning objective functions are mainly determined to maximise economic benefits, environmental benefits, ecological benefits, and social benefits. Finally, the multi-objective genetic algorithm is used to solve the objective function, and the results are used for dynamic programming of sustainable land use. The experimental results show that the ecological benefit coefficient after the proposed method of sustainable land dynamic programming is close to 1, and the error of economic value calculation is reduced.

Keywords: multi-objective genetic algorithm; land use; dynamic programming; population discrete model; grey objective; objective function. (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=139985 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijetma:v:27:y:2024:i:4/5/6:p:356-369

Access Statistics for this article

More articles in International Journal of Environmental Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().