<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper arranges suitable land use types according to the quality characteristics of land parcel units, obtains the objective function of optimal land use allocation, and constructs a multi-objective constraint system in the process of land use planning. The integer vector is used to encode the land use types and represent the spatial distribution of land use, combined with the particle swarm algorithm to calculate its adaptability, speed and position update based on the initial population size and construct the land use optimization allocation model based on the constraint system. The particle swarm algorithm is used to explore the geographic environment and climate resources, the economy of the natural economy of the study area, and Landsat TM remote sensing image technology is used to obtain the initial data on the land use of the study area and empirical analysis is carried out on the optimization of the allocation of urban land use in the study area. The results show that the algorithm converges faster and achieves global convergence when the parameter is set to the fourth state, and the comprehensive suitability evaluation function achieves global convergence at generation 73, with a value of 1.64E+08, which is faster than the convergence of parameter settings 1 and 2 as well as 3. The land types that increase in area include garden land, forest land, transportation land, and water and water conservancy facilities land, which increase by 0.8%, 2.84%, 0.14%, and 0.8%, respectively, which protects the arable land and restricts the expansion of the construction land in terms of the quantitative structure. This study enriches the content and methods of optimizing land use space allocation, which helps to rationalize the use of land resources.</p>
</abstract>

This paper arranges suitable land use types according to the quality characteristics of land parcel units, obtains the objective function of optimal land use allocation, and constructs a multi-objective constraint system in the process of land use planning. The integer vector is used to encode the land use types and represent the spatial distribution of land use, combined with the particle swarm algorithm to calculate its adaptability, speed and position update based on the initial population size and construct the land use optimization allocation model based on the constraint system. The particle swarm algorithm is used to explore the geographic environment and climate resources, the economy of the natural economy of the study area, and Landsat TM remote sensing image technology is used to obtain the initial data on the land use of the study area and empirical analysis is carried out on the optimization of the allocation of urban land use in the study area. The results show that the algorithm converges faster and achieves global convergence when the parameter is set to the fourth state, and the comprehensive suitability evaluation function achieves global convergence at generation 73, with a value of 1.64E+08, which is faster than the convergence of parameter settings 1 and 2 as well as 3. The land types that increase in area include garden land, forest land, transportation land, and water and water conservancy facilities land, which increase by 0.8%, 2.84%, 0.14%, and 0.8%, respectively, which protects the arable land and restricts the expansion of the construction land in terms of the quantitative structure. This study enriches the content and methods of optimizing land use space allocation, which helps to rationalize the use of land resources.

Optimal allocation system of urban land use types under multi-objective constraints

Henan University of Science and Technology

First Affiliated Hospital, Henan University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper arranges suitable land use types according to the quality characteristics of land parcel units, obtains the objective function of optimal land use...

Optimal allocation system of urban land use types under multi-objective constraints

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 02 mar 2023

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01535

Słowa kluczowe
Multi-objective function, Constraint system, Particle swarm algorithm, Adaptability, Land use type

© 2023 Yi Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimal allocation system of urban land use types under multi-objective constraints

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 02 mar 2023

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01535

Słowa kluczoweMulti-objective function, Constraint system, Particle swarm algorithm, Adaptability, Land use type

© 2023 Yi Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Multi-objective function, Constraint system, Particle swarm algorithm, Adaptability, Land use type