Dynamic urban land-use change management using multi-objective evolutionary algorithms
(2020) In Soft Computing: A Fusion of Foundations, Methodologies and Applications 24(6). p.4165-4190- Abstract
Frequent land-use changes in urban areas require an efficient and dynamic approach to reform and update detailed plans by re-arrangement of surrounding land-uses in case of change in one or several urban land-uses. However, re-arrangement of land-uses is problematic, since a variety of conflicting criteria must be considered and satisfied. This paper proposes and examines a two-step approach to resolve the issue. The first step adopts a multi-objective optimization technique to obtain an optimal arrangement of surrounding land-uses in case of change in one or several urban land-uses, whereas the second step uses clustering analysis to produce appropriate solutions for decision makers from the outputs of the first step. To present and... (More)
Frequent land-use changes in urban areas require an efficient and dynamic approach to reform and update detailed plans by re-arrangement of surrounding land-uses in case of change in one or several urban land-uses. However, re-arrangement of land-uses is problematic, since a variety of conflicting criteria must be considered and satisfied. This paper proposes and examines a two-step approach to resolve the issue. The first step adopts a multi-objective optimization technique to obtain an optimal arrangement of surrounding land-uses in case of change in one or several urban land-uses, whereas the second step uses clustering analysis to produce appropriate solutions for decision makers from the outputs of the first step. To present and assess the approach, a case study was conducted in Tehran, the capital of Iran. To satisfy the first step, four conflicting objective functions including maximization of consistency, maximization of dependency, maximization of suitability and maximization of compactness were defined and optimized using non-dominated sorting genetic algorithm. Per-capita demand was also employed as a constraint in the optimization process. Clustering analysis based on ant colony optimization was used to satisfy the second step. The results of the optimization were satisfactory both from a convergence and from a repeatability point of view. Furthermore, the objective functions of optimized arrangements were better than existing land-use arrangement in the area, with the per-capita demand deficiency significantly compensated. The approach was also communicated to urban planners in order to assess its usefulness. In conclusion, the proposed approach can extensively support and facilitate decision making of urban planners and policy makers in reforming and updating existing detailed plans after land-use changes.
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- author
- Masoumi, Zohreh ; Coello Coello, Carlos A. and Mansourian, Ali LU
- organization
- publishing date
- 2020-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Clustering, Decision support, Dynamic urban land-use change, Multi-objective optimization, NSGA-II, Soft computing, Geospatial Artificial Intelligence (GeoAI), Artificial Intelligence (AI)
- in
- Soft Computing: A Fusion of Foundations, Methodologies and Applications
- volume
- 24
- issue
- 6
- pages
- 26 pages
- publisher
- Springer
- external identifiers
-
- scopus:85069749776
- ISSN
- 1432-7643
- DOI
- 10.1007/s00500-019-04182-1
- language
- English
- LU publication?
- yes
- id
- 8f818da1-d1df-4f4c-9982-e11409ab043b
- date added to LUP
- 2019-08-07 12:03:58
- date last changed
- 2023-08-30 12:44:28
@article{8f818da1-d1df-4f4c-9982-e11409ab043b,
abstract = {{<p>Frequent land-use changes in urban areas require an efficient and dynamic approach to reform and update detailed plans by re-arrangement of surrounding land-uses in case of change in one or several urban land-uses. However, re-arrangement of land-uses is problematic, since a variety of conflicting criteria must be considered and satisfied. This paper proposes and examines a two-step approach to resolve the issue. The first step adopts a multi-objective optimization technique to obtain an optimal arrangement of surrounding land-uses in case of change in one or several urban land-uses, whereas the second step uses clustering analysis to produce appropriate solutions for decision makers from the outputs of the first step. To present and assess the approach, a case study was conducted in Tehran, the capital of Iran. To satisfy the first step, four conflicting objective functions including maximization of consistency, maximization of dependency, maximization of suitability and maximization of compactness were defined and optimized using non-dominated sorting genetic algorithm. Per-capita demand was also employed as a constraint in the optimization process. Clustering analysis based on ant colony optimization was used to satisfy the second step. The results of the optimization were satisfactory both from a convergence and from a repeatability point of view. Furthermore, the objective functions of optimized arrangements were better than existing land-use arrangement in the area, with the per-capita demand deficiency significantly compensated. The approach was also communicated to urban planners in order to assess its usefulness. In conclusion, the proposed approach can extensively support and facilitate decision making of urban planners and policy makers in reforming and updating existing detailed plans after land-use changes.</p>}},
author = {{Masoumi, Zohreh and Coello Coello, Carlos A. and Mansourian, Ali}},
issn = {{1432-7643}},
keywords = {{Clustering; Decision support; Dynamic urban land-use change; Multi-objective optimization; NSGA-II; Soft computing; Geospatial Artificial Intelligence (GeoAI); Artificial Intelligence (AI)}},
language = {{eng}},
number = {{6}},
pages = {{4165--4190}},
publisher = {{Springer}},
series = {{Soft Computing: A Fusion of Foundations, Methodologies and Applications}},
title = {{Dynamic urban land-use change management using multi-objective evolutionary algorithms}},
url = {{http://dx.doi.org/10.1007/s00500-019-04182-1}},
doi = {{10.1007/s00500-019-04182-1}},
volume = {{24}},
year = {{2020}},
}