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

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.

Land Use Optimization for Coastal Urban Agglomerations Based on Economic and Ecological Gravitational Linkages and Accessibility

“…GDP is widely used to measure the level of regional development, and many scholars have adopted this objective in land use optimization [13,20,21,52]. Since different land uses correspond to different industries, the study uses the sector-based classification designed by Cao to calculate the GDP per unit area for different land uses [13], as shown in Table 2.…”

“…This paper uses Peng's results of ELI for the Greater Bay Area [51]. Accessibility is expressed by road network density, and its spatial distribution was obtained in arcGIS (Figure 10) after applying the normalization method in Pan's model [21]. Parameters were set as follows: the unit ESV of each land use is derived from the results of Xie's assessment, as shown in Table 3 [56].…”

“…Increased accessibility and transportation infrastructure can improve regional connectivity, social equity, health, and education [18]; however, they can also lead to regional landscape and habitat fragmentation [19], with flow-on impacts to biodiversity. GDP (gross domestic product) and ESV (ecosystem services value) are often used in the calculation of economic and ecological objectives, respectively; however, the above two objectives do not have the ability to select spatial locations, while compactness has such ability to limit land use fragmentation [20,21].…”

“…Optimization of land resources, under multiple conflict objectives, can be modelled using mature intelligent algorithms, such as genetic [21,[32][33][34], simulated annealing [35,36], ant colony [37,38], and particle swarm [15] algorithms. Due to nonlinear tradeoffs between objectives, the overall fitness, representing the balance between objectives, is typically calculated using one of two methods: the weighted-sum method and Pareto frontier.…”

The Application of Genetic Algorithm in Land Use Optimization Research: A Review

“…The researchers of this institution focus on using a variety of different intelligent algorithm models to optimize land use allocation. Pan et al [37] developed a land use intensity-restricted multi-objective spatial optimization model using GA with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. Huang et al [38] coupled a spatial layout evaluation module, system dynamics models, and the multi-agent system to establish the rural settlement consolidation model.…”