Sustainable Land-Use Allocation: A Multiobjective Particle Swarm Optimization Model and Application in Changzhou, China

Abstract

The harmonization of environmental protection with land use for socioeconomic development is one of the major challenges that most Chinese cities are facing in the rapid urbanization and industrialization process. This paper uses a particle swarm optimization algorithm combined with multiple objectives to find the optimum land-use arrangement considering both development and protection objectives and land-use policy constraints simultaneously. The authors consider four objective functions for land-use allocation: maximizing land-use suitability, maximizing land ecosystem service value, maximizing land transformation benefit, and maximizing spatial compactness of land use. These four objective functions are combined into an integrated single-objective function which is used as the fitness function for the particle swarm optimization algorithm in land-use allocation optimization. The authors demonstrate an application of the method with respect to optimizing the arrangement of land-use in Changzhou, China. A set of optimum land-use arrangements is provided by setting varied weight sets to the four objective functions, which are analyzed in terms of the priorities of the four objective functions. Decision makers can select the most appropriate land-use arrangement based on their priorities. The results indicate that the multiobjective particle swarm optimization approach is a promising method for generating land-use spatial allocation alternatives for land-use planning and management.