TY - JOUR
T1 - An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda
AU - Niyomubyeyi, Olive
AU - Veysipanah, Mozafar
AU - Sarwat, Sam
AU - Pilesjö, Petter
AU - Mansourian, Ali
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Multi-objective land-use allocation
KW - Spatial optimization
KW - Sustainable urban planning
KW - None-dominated Sorting Biogeography-based Optimization (NSBBO) algorithm
KW - Operational research
KW - Geospatial Artificial Intelligence (GeoAI)
KW - Artificial Intelligence (AI)
U2 - 10.1080/10095020.2022.2127380
DO - 10.1080/10095020.2022.2127380
M3 - Article
SN - 1009-5020
VL - 27
SP - 968
EP - 982
JO - Geo-Spatial Information Science
JF - Geo-Spatial Information Science
IS - 4
ER -