doi:

DOI: 10.3724/SP.J.1047.2013.00846

Journal of Geo-information Science (地球信息科学学报) 2013/15:6 PP.846-853

Network Voronoi Diagram Heuristic-based Particle Swarm Continuous Spatial Optimization Modeling


Abstract:
Spatial optimization modeling for multi facilities in urbanized area is a practical and key technique, and it can provide balance configuration optimization and spatial decision support for urban public resource. A method of particle swarm spatial optimization modeling for multi facilities location based on network Voronoi diagram heuristic is proposed in this paper, in which we presented respectively some p-median location models and maximal covering location models by using ordinary Voronoi diagram heuristic and network Voronoi diagram heuristic. Those models can quantitatively extract the demands coved by the function and service of facilities through the Voronoi diagrams, and inspire spatial optimization to maximize the coverage for distributed demands by minimizing overlapped coverage. The proposed p-median location model considers the factor of demand attenuation with path distance, and the proposed maximal covering model takes it into account that facility's service provides full coverage for the demands within maximal coverage radius and partial attenuation coverage for the demands without maximal coverage radius. The genetic evolution mechanism and the dynamic neighborhood structure of particles simulated by ordinary Voronoi diagram are integrated in the particle swarm spatial optimization to improve global search and optimization performance of the algorithm. Through the research of spatial optimization configuration experiments for multi facilities in experimental city, the proposed method has been verified to be the effective and practical, it can be applied for the spatial location optimization decision in urbanized area.

Key words:Network Voronoi Diagram,Spatial Optimization Modeling,Diagram Heuristic,Multi-facilities Location Particle Swarm Optimization

ReleaseDate:2015-04-17 13:34:28



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