热带地理

TROPICAL GEOGRAPHY ›› 2017, Vol. 37 ›› Issue (3): 383-391.doi: 10.13284/j.cnki.rddl.002911

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Spatial Optimization for Emergency Materials Allocation Based on a Self-adaptive Binary-state PSO Algorithm

CHEN Tongli1，MA Shifa2，XU Shuyuan3，LI Haibo1   

  1. （1．Dongguan Geographic Information and Urban Planning Researching Center，Dongguan 523129，China；2．Land Resources Technology Center of Guangdong Provence，Guangzhou 510075，China；3．School of Geography and Planning，Sun Yat-sen University，Guangzhou 510275，China）
  • Online:2017-05-05 Published:2017-05-05

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Abstract: The allocation of emergency materials is a typical spatial optimization decision problem for social service. A hybrid model was proposed in this paper to allocate the emergency materials based on GIS and particle swarm optimization algorithm. In view of the drawbacks that it is easy to fall into local best solutions with the increasing dimensions for classical PSO, the evolution curvature was defined as population diversity, and a binary-state evolution mechanism, which includes the “predation” and “exploration” states, was adopted. In addition, the variation operator was designed just only for the elite particles, which can well balance the group learning and exploration behavior in outer space. Furthermore, the constraint for this spatial optimization was set by the combination of dynamic recurrent and the random characteristic of swarm intelligent algorithms. This model has been tested by using a theoretical datasets with low dimensions and high dimensions respectively. It has shown that the spatial optimization model designed in this paper can get global optimum solution for fewer and also perform well for larger dataset. Finally, a case study was implemented in a city. Results demonstrate that the spatial optimization model coupled with GIS and swarm intelligence algorithm would be an important application for the smart city’s construction.

Key words: particle swarm optimization, spatial optimization, self-adaptive, emergency materials, smart city