Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization

Bibliographic Data

ID22032089
AuthorsTingting Pan (0000-0002-8538-4212, Chinese Academy of Sciences), Yu Zhang (0000-0002-0059-270X, Chinese Academy of Sciences), Fenzhen Su (0000-0003-4972-3595, Chinese Academy of Sciences, corresponding author), Vincent Lyne (0000-0002-7718-4050, University of Tasmania), Fei Cheng (0000-0003-4402-4509, Xiamen University), Han Xiao (0000-0003-2895-1847, Chinese Academy of Sciences)
Year2021
Volume10
Issue2
Pages100
Publication date2021-02-22
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10020100
OpenAlexW3131080542
LanguageEN
Citations received6
References cited33

Practical efficient regional land-use planning requires planners to balance competing uses, regional policies, spatial compatibilities, and priorities across the social, economic, and ecological domains. Genetic algorithm optimization has progressed complex planning, but challenges remain in developing practical alternatives to random initialization, genetic mutations, and to pragmatically balance competing objectives. To meet these practical needs, we developed a Land use Intensity-restricted Multi-objective Spatial Optimization (LIr-MSO) model with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. We tested the model for Dapeng, China where experiments compared comprehensive fitness (across conversion cost, Gross Domestic Product (GDP), ecosystem services value, compactness, and conflict degree) with three contrast experiments, in which changes were separately made in the initialization and mutation. The comprehensive model gave superior fitness compared to the contrast experiments. Iterations progressed rapidly to near-optimality, but final convergence involved much slower parent–offspring mutations. Tradeoffs between conversion cost and compactness were strongest, and conflict degree improved in part as an emergent property of the spatial social connectedness built into our algorithm. Observations of rapid iteration to near-optimality with our model can facilitate interactive simulations, not possible with current models, involving land-use planners and regional managers

Genetic algorithm · Initialization · Land use · Land-use planning · Machine learning · Mathematical optimization · Operations research · Randomness · Computer Science · Economic and Environmental Valuation · Land Use and Ecosystem Services · Mathematics · Urban Planning and Valuation · Ecology

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