eCite Digital Repository

Practical efficient regional land-use planning using constrained multi-objective genetic algorithm optimization

Citation

Pan, T and Zhang, Y and Su, F and Lyne, V and Cheng, F and Xiao, H, Practical efficient regional land-use planning using constrained multi-objective genetic algorithm optimization, ISPRS International Journal of Geo-Information, 10, (2) Article 100. ISSN 2220-9964 (2021) [Refereed Article]


Preview
PDF (Published version)
6Mb
  

Copyright Statement

Copyright: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

DOI: doi:10.3390/ijgi10020100

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:land-use optimization, genetic algorithm, spatial compactness
Research Division:Built Environment and Design
Research Group:Urban and regional planning
Research Field:Land use and environmental planning
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Evaluation, allocation, and impacts of land use
UTAS Author:Lyne, V (Dr Vincent Lyne)
ID Code:150747
Year Published:2021
Web of Science® Times Cited:2
Deposited By:Fisheries and Aquaculture
Deposited On:2022-06-27
Last Modified:2022-07-28
Downloads:2 View Download Statistics

Repository Staff Only: item control page