Research On Location Selection Of Emergency Shelter Based On Ant Colony Algorithm

With the acceleration of urbanization,cities have gradually formed a situation of population density expansion and spatial mechanical expansion.In today’s increasingly tense land use,weak urban infrastructure,dense spatial structure,and the consequences of various disasters have been multiplied.When a disaster occurs,the emergency shelter is one of the destinations for evacuating people.The rationality of its location construction is directly related to the safety of life and property of the urban population and the sustainable development of the city.There are many methods for the location selection of emergency shelters.As a swarm intelligence algorithm,ant colony algorithm has significant advantages in solving discrete complex geographic optimization problems.However,traditional ant colony algorithm is easy to fall into local optimum and the solution speed is slow.Based on the above situation,this paper studies the location of emergency shelters,and focuses on the optimization and improvement of ant colony algorithm in the location of emergency shelters.The main work and achievements of the paper include the following aspects:(1)Build a road emergency evacuation index and establish a site selection model for emergency shelters.Based on the research of relevant scholars,this paper selects seven factors for the location of emergency shelters,which are population density,distance from high-rise buildings,distance from main roads,road width,road node degree,and high-rise buildings.Threat and pedestrian backflow,in which the road emergency evacuation index is constructed for road width,road node degree,threat of high-rise buildings and pedestrian backflow,to replace the road network distance or straight-line distance in the traditional method.The experimental results show that the emergency shelter location model constructed in this paper is reasonable and feasible.(2)Optimize the ant colony algorithm.This paper optimizes the pheromone update rules and tabu table adjustment rules in the traditional ant colony algorithm,and proposes a calculation method of step-bystep approximation,which solves the problem that the ant colony algorithm is easy to fall into local optimum and appears premature.Operational efficiency and result accuracy.(3)Java programming language experiment site selection service.This paper uses the Java programming language to realize the realization of traditional ant colony algorithm and optimized ant colony algorithm,and takes the Longzihu Sub-district Office and Boxue Road Sub-district Office of Jinshui District,Zhengzhou City as the experimental area to select five emergency shelters.In the experiment,compared with the traditional ant colony algorithm site selection results,the results show that the site selection results under the optimized ant colony algorithm are more reasonable,and the calculation speed is increased by about 20%.The research results of the paper enrich the theoretical basis of the ant colony algorithm and the location of emergency shelters,and also provide a reference for the government to build emergency shelters.