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An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda

Niyomubyeyi, Olive LU ; Veysipanah, Mozafar LU ; Sarwat, Sam LU ; Pilesjö, Petter LU and Mansourian, Ali LU (2022) In Geo-Spatial Information Science p.1-15
Abstract
With the continuous increase of rapid urbanization and population growth, sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers. Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives. This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization (NSBBO) algorithm for solving the multi-objective land-use allocation problem, in which maximum accessibility, maximum compactness, and maximum spatial integration were formulated as spatial objectives; and space syntax analysis was used to analyze the potential movement patterns in... (More)
With the continuous increase of rapid urbanization and population growth, sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers. Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives. This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization (NSBBO) algorithm for solving the multi-objective land-use allocation problem, in which maximum accessibility, maximum compactness, and maximum spatial integration were formulated as spatial objectives; and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali, Rwanda. Efficient Non-dominated Sorting (ENS) algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions, and local search ability, and to accelerate the convergence speed of the algorithm. The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO. Furthermore, the proposed algorithm could generate optimal land use scenarios according to the preferred objectives, thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use. (Less)
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Multi-objective land-use allocation, Spatial optimization, Sustainable urban planning, None-dominated Sorting Biogeography-based Optimization (NSBBO) algorithm, Operational research, Geospatial Artificial Intelligence (GeoAI), Artificial Intelligence (AI)
in
Geo-Spatial Information Science
pages
1 - 15
publisher
Taylor & Francis
external identifiers
  • scopus:85141992081
ISSN
1009-5020
DOI
10.1080/10095020.2022.2127380
language
English
LU publication?
yes
id
0575cddc-7319-4fb5-bc7c-82ec1fb0ae7b
date added to LUP
2022-11-25 11:25:51
date last changed
2023-10-09 08:20:43
@article{0575cddc-7319-4fb5-bc7c-82ec1fb0ae7b,
  abstract     = {{With the continuous increase of rapid urbanization and population growth, sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers. Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives. This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization (NSBBO) algorithm for solving the multi-objective land-use allocation problem, in which maximum accessibility, maximum compactness, and maximum spatial integration were formulated as spatial objectives; and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali, Rwanda. Efficient Non-dominated Sorting (ENS) algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions, and local search ability, and to accelerate the convergence speed of the algorithm. The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO. Furthermore, the proposed algorithm could generate optimal land use scenarios according to the preferred objectives, thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use.}},
  author       = {{Niyomubyeyi, Olive and Veysipanah, Mozafar and Sarwat, Sam and Pilesjö, Petter and Mansourian, Ali}},
  issn         = {{1009-5020}},
  keywords     = {{Multi-objective land-use allocation; Spatial optimization; Sustainable urban planning; None-dominated Sorting Biogeography-based Optimization (NSBBO) algorithm; Operational research; Geospatial Artificial Intelligence (GeoAI); Artificial Intelligence (AI)}},
  language     = {{eng}},
  month        = {{11}},
  pages        = {{1--15}},
  publisher    = {{Taylor & Francis}},
  series       = {{Geo-Spatial Information Science}},
  title        = {{An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda}},
  url          = {{http://dx.doi.org/10.1080/10095020.2022.2127380}},
  doi          = {{10.1080/10095020.2022.2127380}},
  year         = {{2022}},
}