Abstract
Optimizing land-use allocation is important to regional sustainable development, as it promotes the social equality of public services, increases the economic benefits of land-use activities, and reduces the ecological risk of land-use planning. Most land-use optimization models allocate land-use using cell-level operations that fragment land-use patches. These models do not cooperate well with land-use planning knowledge, leading to irrational land-use patterns. This study focuses on building a heuristic land-use allocation model (PSOLA) using particle swarm optimization. The model allocates land-use with patch-level operations to avoid fragmentation. The patch-level operations include a patch-edge operator, a patch-size operator, and a patch-compactness operator that constrain the size and shape of land-use patches. The model is also integrated with knowledge-informed rules to provide auxiliary knowledge of land-use planning during optimization. The knowledge-informed rules consist of suitability, accessibility, land use policy, and stakeholders’ preference. To validate the PSOLA model, a case study was performed in Gaoqiao Town in Zhejiang Province, China. The results demonstrate that the PSOLA model outperforms a basic PSO (Particle Swarm Optimization) in the terms of the social, economic, ecological, and overall benefits by 3.60%, 7.10%, 1.53% and 4.06%, respectively, which confirms the effectiveness of our improvements. Furthermore, the model has an open architecture, enabling its extension as a generic tool to support decision making in land-use planning.
Highlights
Land-use allocation is a process of allocating different activities or uses to specific units of area within a geospatial context, to maximize a spectrum of social, economic, and ecological benefits [1]
This study focuses on building a heuristic land-use allocation model using particle swarm optimization (PSO)
Many heuristic land-use allocation models such as the genetic algorithm (GA) [1, 17, 28, 41], ant colony optimization (ACO) [18], artificial immune systems (AIS) [33, 42] and particle swarm optimization (PSO) [19, 20, 22], are global optimization models that pursue overall optimal solutions to satisfy a host of global objectives
Summary
Land-use allocation is a process of allocating different activities or uses to specific units of area within a geospatial context, to maximize a spectrum of social, economic, and ecological benefits [1]. Heuristic methods are capable of generating near-optimal solutions with an acceptable time cost They have no limitations to the form of the objectives and constraints. Many land-use allocation models have been developed using heuristic methods: Aerts [16] applied simulated annealing (SA) to the multi-objective site selection problem; Cao and Batty [17] utilized a genetic algorithm (GA) to generate several optimized solutions in urban land-use allocation; Liu [18] introduced ant colony optimization (ACO) for zoning protected ecological areas; and Hu [19] developed a particle swarm optimization (PSO) model for the optimal allocation of earthquake emergency shelters. Most land use optimization models with heuristic methods, allocate land use using cell-level operations.
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