Delimiting the urban growth boundaries with a modified ant colony optimization model

Abstract Delimiting urban growth boundaries (UGBs) has been generally regarded as a regulatory measure for controlling chaotic urban expansion. There are increasing demands for delimiting urban growth boundaries in fast growing regions in China. However, existing methods for delimiting UGBs mainly focus on intrinsic dynamic processes of urban growth and ignore external planning interventions. Delimiting UGBs to restrain chaotic expansion and conserve ecological areas is actually a spatial optimization problem. This study aims to develop an optimization-based framework for delimiting optimal UGBs by incorporating dynamic processes and planning interventions into an ant colony optimization (ACO) algorithm. Local connectivity, total utility values and quantity assignment were integrated into the exchange mechanism to make ACO adaptive for the delimitation of UGBs. The core area of Changsha-Zhuzhou-Xiangtan urban agglomeration, a very fast growing area in Central China was selected as the case study area to validate the proposed model. UGBs under multi planning scenarios with given combinations of weights for urban suitability, high-quality farmland protection, and landscape compactness were efficiently derived from the ACO model. Hypothetic datasets were initially used to test the performance of ACO on global optimum and its ability to optimize complex landscape patterns. Compared with experts' planning scenario, the optimal UGBs delimited by ACO model is practical. Results indicate that spatial optimization methods are plausible for delimiting optimal UGBs.