Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization
文献类型：期刊论文
| 作者 | Pan, Tingting1,4,5; Zhang, Yu5; Su, Fenzhen3,4,5; Lyne, Vincent2; Cheng, Fei6; Xiao, Han1,4,5 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2021-02-01 |
| 卷号 | 10期号:2页码:21 |
| 关键词 | land-use optimization genetic algorithm spatial compactness |
| DOI | 10.3390/ijgi10020100 |
| 通讯作者 | Su, Fenzhen(sufz@lreis.ac.cn) |
| 英文摘要 | Practical efficient regional land-use planning requires planners to balance competing uses, regional policies, spatial compatibilities, and priorities across the social, economic, and ecological domains. Genetic algorithm optimization has progressed complex planning, but challenges remain in developing practical alternatives to random initialization, genetic mutations, and to pragmatically balance competing objectives. To meet these practical needs, we developed a Land use Intensity-restricted Multi-objective Spatial Optimization (LIr-MSO) model with more realistic patch size initialization, novel mutation, elite strategies, and objectives balanced via nominalizations and weightings. We tested the model for Dapeng, China where experiments compared comprehensive fitness (across conversion cost, Gross Domestic Product (GDP), ecosystem services value, compactness, and conflict degree) with three contrast experiments, in which changes were separately made in the initialization and mutation. The comprehensive model gave superior fitness compared to the contrast experiments. Iterations progressed rapidly to near-optimality, but final convergence involved much slower parent-offspring mutations. Tradeoffs between conversion cost and compactness were strongest, and conflict degree improved in part as an emergent property of the spatial social connectedness built into our algorithm. Observations of rapid iteration to near-optimality with our model can facilitate interactive simulations, not possible with current models, involving land-use planners and regional managers. |
| 资助项目 | National Natural Science Foundation of China[41890854] |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000622579400001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/160517]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Su, Fenzhen |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Univ Tasmania, Inst Marine & Antarctic Studies, Hobart, Tas 7004, Australia 3.Chinese Acad Sci, Innovat Acad South China Sea Ecol & Environm Engn, Guangzhou 510301, Peoples R China 4.Nanjing Univ, Collaborat Innovat Ctr South China Sea Studies, Nanjing 210093, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Xiamen Univ, Coll Environm & Ecol, Key Lab, Minist Educ Coastal Wetland Ecosyst, Xiamen 361102, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pan, Tingting,Zhang, Yu,Su, Fenzhen,et al. Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2021,10(2):21. |
| APA | Pan, Tingting,Zhang, Yu,Su, Fenzhen,Lyne, Vincent,Cheng, Fei,&Xiao, Han.(2021).Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,10(2),21. |
| MLA | Pan, Tingting,et al."Practical Efficient Regional Land-Use Planning Using Constrained Multi-Objective Genetic Algorithm Optimization".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10.2(2021):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。