为探索一种客观、量化且能解决多目标决策问题的土地利用空间优化配置方法,在"资源节约"与"环境友好"目标约束下,设计应用于土地利用空间优化配置的多智能体遗传进化算法,构建多目标土地利用空间优化配置MOSOLUA(Mu...为探索一种客观、量化且能解决多目标决策问题的土地利用空间优化配置方法,在"资源节约"与"环境友好"目标约束下,设计应用于土地利用空间优化配置的多智能体遗传进化算法,构建多目标土地利用空间优化配置MOSOLUA(Multi objective spatial optimization model for land use allocation)模型;以国家资源节约型和环境友好型社会建设综合配套改革实验区——长株潭城市群的核心区域为例,进行多目标土地利用空间优化配置应用研究。研究结果表明:基于MOSOLUA模型得到的优化后的土地利用格局的资源节约与环境友好程度较优化前有明显提高;MOSOLUA模型的收敛速度较普通遗传算法模型的快,实证应用所花时间由8.57 h减少到3.31 h,运行效率提高61.38%;模型的总体适应度与采用普通遗传算法的优化配置模型相比提高了12.57%。展开更多
Semiarid loess hilly areas in China are enduring a series of environmental conflicts between urban expansion,cultivated land conservation,soil erosion and water shortage,and require land use allocation to reconcile th...Semiarid loess hilly areas in China are enduring a series of environmental conflicts between urban expansion,cultivated land conservation,soil erosion and water shortage,and require land use allocation to reconcile these environmental conflicts.We argue that the optimized spatial allocation of rural land use can be achieved by a Particle Swarm Optimization (PSO) model in conjunction with multi-objective optimization techniques.Our study focuses on Yuzhong County of Gangsu Province in China,a typical catchment on the Loess Plateau,and proposes a land use spatial optimization model.The model maximizes land use suitability and spatial compactness based on a variety of constraints,e.g.optimal land use structure and restrictive areas,and employs an improved PSO algorithm equipped with a determinant initialization method and a dynamic weighted aggregation (DWA) method to obtain the optimized land use spatial pattern.The results suggest that (1) approximately 4% of land use should be reallocated and these changes would alleviate the environmental conflicts in the study area;(2) the major reshuffling is slope farmland and newly added construction and cultivated land,whereas the unchanged areas are largely forests and basic farmland;and (3) the PSO is capable of optimizing rural land use allocation,and the determinant initialization method and DWA can improve the performance of the PSO.展开更多
文摘为探索一种客观、量化且能解决多目标决策问题的土地利用空间优化配置方法,在"资源节约"与"环境友好"目标约束下,设计应用于土地利用空间优化配置的多智能体遗传进化算法,构建多目标土地利用空间优化配置MOSOLUA(Multi objective spatial optimization model for land use allocation)模型;以国家资源节约型和环境友好型社会建设综合配套改革实验区——长株潭城市群的核心区域为例,进行多目标土地利用空间优化配置应用研究。研究结果表明:基于MOSOLUA模型得到的优化后的土地利用格局的资源节约与环境友好程度较优化前有明显提高;MOSOLUA模型的收敛速度较普通遗传算法模型的快,实证应用所花时间由8.57 h减少到3.31 h,运行效率提高61.38%;模型的总体适应度与采用普通遗传算法的优化配置模型相比提高了12.57%。
基金supported in part by the National High-Tech Research & Development Program of China (Grant No.2011AA120304)National Key Technology R&D Program of China(Grant Nos. 2011BAB01B06 and 2006BAB05B06)
文摘Semiarid loess hilly areas in China are enduring a series of environmental conflicts between urban expansion,cultivated land conservation,soil erosion and water shortage,and require land use allocation to reconcile these environmental conflicts.We argue that the optimized spatial allocation of rural land use can be achieved by a Particle Swarm Optimization (PSO) model in conjunction with multi-objective optimization techniques.Our study focuses on Yuzhong County of Gangsu Province in China,a typical catchment on the Loess Plateau,and proposes a land use spatial optimization model.The model maximizes land use suitability and spatial compactness based on a variety of constraints,e.g.optimal land use structure and restrictive areas,and employs an improved PSO algorithm equipped with a determinant initialization method and a dynamic weighted aggregation (DWA) method to obtain the optimized land use spatial pattern.The results suggest that (1) approximately 4% of land use should be reallocated and these changes would alleviate the environmental conflicts in the study area;(2) the major reshuffling is slope farmland and newly added construction and cultivated land,whereas the unchanged areas are largely forests and basic farmland;and (3) the PSO is capable of optimizing rural land use allocation,and the determinant initialization method and DWA can improve the performance of the PSO.
