多目标土地利用空间优化配置模型及其应用

Multi-objective spatial optimization model for land use allocation and its application

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%。

Under the constraint of resource-saving and environment-friendliness objective,based on multi-agent genetic algorithm,multi-objective spatial optimization model for land use allocation was developed by simulating the biological autonomous adaptability to environment and the competitive and cooperative relation.This model was tested in the core area of Changsha,Zhuzhou,Xiangtan city cluster,which is the national comprehensive reforms test areas of building resource-saving and environment-friendliness society in China.The results show that integration time of MOSOLUM model and standaral genetic algorithm are separately 3.31 h and 8.57 h,which relocks an improvement of 61.38% in running efficiency of MOSOLUA model compared with the standard genetic algorithm model,and there is an increase of 12.57% in total fitness of MOSOLUA model compared with the standaral algorithm model.The results indicate that multi-objective spatial optimization model for land use allocation is a promising method for generating land-use alternatives for further consideration in spatial decision-making.