A improved particle swarm optimization based on cloud model with implications for urban land use planning

2009 ◽  
Author(s):  
Yanfei Wei ◽  
Yaolin Liu ◽  
Dun Wang
Keyword(s):  
Land Use ◽  
Particle Swarm Optimization ◽  
Land Use Planning ◽  
Cloud Model ◽  
Particle Swarm ◽  
Urban Land ◽  
Urban Land Use ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

scholarly journals Study on the planning of rural land spatial utilization by improved particle swarm optimization

2020 ◽  
Vol 44 (6) ◽  
pp. 990-994
Author(s):  
W.Z. Yi
Keyword(s):  
Particle Swarm Optimization ◽  
Land Use Planning ◽  
Particle Swarm ◽  
Space Use ◽  
Space Distribution ◽  
Rural Land ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ipso Algorithm ◽  
Land Space

The planning of rural land space utilization is a very important problem. In this paper, the objective function of rural land use planning was analyzed firstly, and then the improved particle swarm optimization (IPSO) algorithm was obtained by improving the inertia weight for solution. The results showed that the land space use in the study area was more reasonable after the planning based on the IPSO algorithm, the forest land and construction land increased, the area of grassland, cultivated land and water area reduced appropriately, the aggregation degree of all types of land improved, and the space distribution was more planned, which was more conducive to production activities. The analysis results verify the effectiveness of the IPSO method in land space use planning, which can improve the efficiency and benefit of land space use, and it can be popularized in practical application.

IMPACTS OF URBAN LAND USE ON CRIME PATTERNS THROUGH GIS APPLICATION

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Ahmad Nazri Muhamad Ludin ◽  
Norsiah Abd. Aziz ◽  
Nooraini Hj Yusoff ◽  
Wan Juliyana Wan Abd Razak
Keyword(s):  
Land Use ◽  
Drug Abuse ◽  
Land Use Planning ◽  
Urban Areas ◽  
Spatial Relationship ◽  
Police Department ◽  
Strong Relationship ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Patterns

Land use planning plays a crucial role in creating a balance between the needs of society, physical development and the ecosystem. However, most often poor planning and displacement of land uses particularly in urban areas contribute to social ills such as drug abuse and criminal activities. This research explains the spatial relationship of drug abuse and other criminal activities on urban land use planning and their implications on the society at large. Spatial statistics was used to show patterns, trends and spatial relationships of crimes and land use planning. Data on crime incidents were obtained from the Royal Malaysia Police Department whilst cases of drug abuse were collected from the National Anti-Drug Agency (AADK). Analysis of the data together with digital land use maps produced by Arnpang Jaya Municipal Council, showed the distribution of crime incidents and drug abuse in the area. Findings of the study also indicated that, there was a strong relationship between petty crimes, drng abuse and land use patterns. These criminal activities tend to concentrate in residential and commercial areas of the study area.

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

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