A Framework with Improved Spatial Optimization Algorithms to Support China’s “Multiple-plan Integration” Planning at the County Level

Abstract

The “Multiple-plan integration” planning is proposed by the Chinese government to coordinate various planning projects in managing spatial development and protecting agricultural and ecological resources. It can be treated as a multi-objective land allocation (MOLA) problem that aims to optimize the land allocation pattern to maximize land suitability for agriculture, construction, and conservation while encouraging compact land allocation. However, there is a lack of applicable methodology or frameworks to support the “Multiple-plan integration” planning at the county level. This dissertation is intended to develop and test a framework to solve this “Multiple-plan integration” problem with improved spatial optimization algorithms. The criteria for land suitability evaluation in China’s county-level “Multiple-plan integration” were first reviewed and established. Then, the performance of three classical heuristic optimization algorithms including simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO) was compared in solving a simplified MOLA problem. The comparison results show that classical NSGA-II in the GA family performs the best, but its computational cost is high in maintaining compact land allocation. Next, an improved knowledge-informed NSGA-II was developed by integrating patch-based, edge growing/decreasing, neighborhood, and constraint steering rules. The improved algorithm is more effective and efficient than classical NSGA-II in encouraging compact land allocation while its capability of maximizing land suitability is not sacrificed. Finally, a Multiple-plan Integration with Spatial Optimization (MPI-SOP) framework was proposed to support China’s “Multiple-plan integration” planning at the county level. This framework is composed of five steps: mathematically formulating the spatial optimization problem, land suitability evaluation, optimization problem solving, post-processing of land allocation solutions, and applying post-processed solutions to planning. The spatial optimization problem was solved by a patch-based and knowledge-informed NSGA-II. The case study in Dongxihu District of Wuhan City shows that the framework is feasible and effective in supporting the “Multiple-plan integration” decision making. This dissertation has made two major contributions. Practically, it has proposed and tested a framework to support China’s “Multiple-plan integration” planning with spatial optimization at the county level; methodologically, knowledge-informed heuristic optimization algorithms have been developed to solve the MOLA problem more effectively and efficiently.