Improvement Of Bacterial Foraging Optimization Algorithm And Its Application In Emergency Resources Scheduling Of Chemical Industry Parks

With the continuous acceleration of the agglomeration and development of the chemical industry,the number of chemical parks continues to increase,and scale and parkization have become a necessary stage of my country’s industrialization process.The large-scale and industrial parks of the chemical industry not only improve the production efficiency of the industry,but also bring many challenges to emergency scheduling in the event of an accident.Emergency resource scheduling is the most important link in the emergency rescue process.Once it is not handled well,it will pay a painful price.Therefore,in order to reduce the loss caused by the accident.It is of great significance to carry out in-depth research on the emergency resource scheduling of the chemical park accident.Due to the complexity and dynamics of the emergency scheduling problems,it is difficult to solve them using traditional operations research methods or heuristic algorithms.In recent years,more and more scholars have chosen to use swarm intelligence optimization algorithms to solve these problems.Although the swarm intelligence optimization algorithm may not be able to obtain the optimal solution of the problem within the specified time,it can obtain a better satisfactory solution and does not depend on the initial solution.Bacterial foraging optimization algorithm is a swarm intelligence optimization algorithm proposed to simulate the behavior of bacteria.Its code is simple and easy to understand,and it has good optimization potential.At the same time,the intelligent behavior of bacterial colonies has strong characteristics of complexity,dynamics and chaos,which are similar to the characteristics of emergency scheduling problems.Therefore,this paper intends to use the bacterial foraging optimization algorithm to solve the emergency scheduling problem of chemical park accidents.This paper first improved the bacterial foraging optimization algorithm.Then we built an emergency resources scheduling model based on chemical park accidents.Finally,using the improved bacterial foraging optimization algorithm to solve the emergency resources scheduling problem.The main contents are as follows:Firstly,this paper conducts an in-depth analysis of the bacterial foraging optimization algorithm and analyzes the reasons for its slow convergence and lack of interaction among individuals.For the reasons of its shortcomings,the three-layer nested loop structure of the bacterial foraging optimization algorithm is replaced with single layer circulation structure.In chemotaxis operations,an individual interaction strategy based on relative position update is introduced.In migration operations,a migration strategy based on relative position update is introduced.Then the relative position-based bacterial foraging optimization algorithm is proposed.And we use 11 benchmark functions to test the performance of the relative positionbased bacterial foraging optimization algorithm.Compared with the particle swarm optimization algorithm,bacterial foraging optimization algorithm,and genetic algorithm,the results show that the relative position-based bacterial foraging optimization algorithm obtains the faster convergence speed and better solution accuracy,which verify the effectiveness of the algorithm.Secondly,the resources are divided into two types.One is transported by a transport vehicle.The other is not need to be transported by a transport vehicle.For the resources that need to be transported by a transport vehicle,we not only consider the travel time,but also consider the preparation time.On this basis,this article takes the fire accident in the chemical industry park as the background,takes emergency rescue time and transportation costs as the optimization goals,and considers the storage capacity constraints of rescue points,the demand constraints of emergency points,the failure time constraints and the non-negative integer constraints of variables.An emergency scheduling model with multiple emergency points,multiple rescue points,and multiple resources is established.Finally,with the design of the solution coding method,constraint processing rules,boundary control strategy,etc.,the algorithm is suitable for the established discrete and multiconstrained emergency scheduling model.Use the designed algorithm to test on multiple examples,and compare the results with particle swarm algorithm,bacterial foraging algorithm,and genetic algorithm.The result showed that in all the examples,the relative position-based bacterial foraging optimization algorithm obtains better convergence accuracy and faster convergence speed.The validity and practicability of the constructed model and improved algorithm are proved.