Excessive unplanned urban growth leads to many vulnerabilities and impacts on urban environments to varying degrees. However, the majority of the extant literature focuses on the problems related to location and socio...Excessive unplanned urban growth leads to many vulnerabilities and impacts on urban environments to varying degrees. However, the majority of the extant literature focuses on the problems related to location and socioeconomic conditions, rather than vulnerability processes and related environmental degradation. This paper analyzes the scope of urban vulnerabilities for five rapidly urbanizing and highly-congested cities in the Kathmandu Valley, Nepal. First, the historic context of the Valley’s uncontrolled urbanization sets the scene. Second, the optic is narrowed to focus upon the geographical features of the resultant urbanized Valley landscape that includes spatial arrangements and of houses, population densities, road networks, vehicular densities, garbage problems, and available open spaces. Additionally, seismic vulnerabilities in the urban areas are also considering in this examination. Third, three-dimensional visualizations of selected urban locations are presented to differentiate between vulnerable and relatively safe locations. The intent of this research is to contribute to the methodological understanding of human/hazards interactions in rapidly urbanizing cities of the Third World, which share similar socioeconomic conditions and environmental con-texts.展开更多
研究三维空间机器人路径规划问题,由于系统求解时间较长、过早失去解的多样性、易陷入局部最优、个体适应度较差等问题,通过构建三维工作空间模型、引入变异算子和搜索无碰路径策略来解决,提出适宜于三维机器人路径规划的一种变异算子...研究三维空间机器人路径规划问题,由于系统求解时间较长、过早失去解的多样性、易陷入局部最优、个体适应度较差等问题,通过构建三维工作空间模型、引入变异算子和搜索无碰路径策略来解决,提出适宜于三维机器人路径规划的一种变异算子蚁群算法(Mutation Operator Ant Colony Algorithm,MOACA)。MOACA是一种关于模型构造的启发式搜索算法,算法在改进启发式函数设计、选择概率确定、信息素更新策略等基础上,引入逆转变异和插入变异算子,通过选择逆转点反序排列部分路径节点和随机插入路径节点的方法搜索无碰路径,对蚁群算法进行了局部优化改良。仿真结果表明,MOACA在搜索路径、收敛时间、适应度等方面较传统蚁群算法有明显改善,算法是有效可行的。展开更多
文摘Excessive unplanned urban growth leads to many vulnerabilities and impacts on urban environments to varying degrees. However, the majority of the extant literature focuses on the problems related to location and socioeconomic conditions, rather than vulnerability processes and related environmental degradation. This paper analyzes the scope of urban vulnerabilities for five rapidly urbanizing and highly-congested cities in the Kathmandu Valley, Nepal. First, the historic context of the Valley’s uncontrolled urbanization sets the scene. Second, the optic is narrowed to focus upon the geographical features of the resultant urbanized Valley landscape that includes spatial arrangements and of houses, population densities, road networks, vehicular densities, garbage problems, and available open spaces. Additionally, seismic vulnerabilities in the urban areas are also considering in this examination. Third, three-dimensional visualizations of selected urban locations are presented to differentiate between vulnerable and relatively safe locations. The intent of this research is to contribute to the methodological understanding of human/hazards interactions in rapidly urbanizing cities of the Third World, which share similar socioeconomic conditions and environmental con-texts.
文摘研究三维空间机器人路径规划问题,由于系统求解时间较长、过早失去解的多样性、易陷入局部最优、个体适应度较差等问题,通过构建三维工作空间模型、引入变异算子和搜索无碰路径策略来解决,提出适宜于三维机器人路径规划的一种变异算子蚁群算法(Mutation Operator Ant Colony Algorithm,MOACA)。MOACA是一种关于模型构造的启发式搜索算法,算法在改进启发式函数设计、选择概率确定、信息素更新策略等基础上,引入逆转变异和插入变异算子,通过选择逆转点反序排列部分路径节点和随机插入路径节点的方法搜索无碰路径,对蚁群算法进行了局部优化改良。仿真结果表明,MOACA在搜索路径、收敛时间、适应度等方面较传统蚁群算法有明显改善,算法是有效可行的。
