Improved Bee Colony Algorithm And Its Application In Crowd Evacuation Route Planning

With the acceleration of the process of industrialization and modernization,the convenience of people's life has also brought some hidden dangers.The rapid and orderly evacuation of the crowd is critical when the crisis occurs.However,the existing crowd evacuation drills are difficult to arrange the best escape route for everyone,and thus cannot be easily evacuated without congestion.The problem of path planning concerns the vital interests of everyone,and it is an important problem to be solved urgently.The artificial bee colony algorithm(ABC algorithm)was proposed by Turkish scholar Karaboga in the year 2005.Like other intelligent algorithms,ABC algorithm has the advantages of simple principle,less control parameters,good flexibility,and high adaptability,which has caused the attention of scholars all over the world.Now it has been widely used in function optimization,image processing,data mining,path planning and other fields.However,when solving complex optimization problems,the artificial bee colony algorithm has the defects of poor local search ability and slow convergence speed.Therefore,if it is used directly in the path planning in crowd evacuation,the accuracy and efficiency of crowd evacuation are not good.This paper aims to improve the speed and precision of path planning.According to the particularity of evacuation crowds,the defects of the algorithm itself and the crowd behavior in evacuation are considered separately,and two different improvements are proposed.The performance of the improved algorithm was tested using classic test functions and similar algorithms,and the efficiency of evacuation was finally verified through simulation experiments.The main work and innovation of this article are as follows:1.Analyze the way of updating the position of the leading bee and the following bee to determine the reason why the original bee colony algorithm is slow in convergence and easy to fall into the local optimum.In order to overcome the above shortcomings,this paper uses the global optimization theory of particle swarm optimization and the segmented search strategy to improve the algorithm.A new bee colony algorithm(3SABC algorithm)based on segmented search strategy is proposed.2.This article starts from the behavior pattern of natural bees and considering the influence of external factors on the colony.Then a novel bee colony algorithm based on floral concentration(FFABC algorithm)was proposed.The algorithm fits natural instincts that natural bee colonies perceive through the concentration of floral scent and goes to a position where a better honey source may exist,and takes into account the influence of external factors(wind factors,bee physical strength)on the bee colony's step size.3.In this paper,the improved bee colony algorithm is used to implement the path planning for the evacuation group,which can generate a safe and efficient escape route for each individual.