Improvement Of Grey Wolf Optimizer And Its Application In Leakage Source Location

Grey Wolf Optimizer(GWO)is one of the new swarm intelligence algorithms proposed in recent years.Its idea is derived from the predatory behavior of gray wolves in nature.After it was proposed,GWO attracted the attention of many scholars due to its uncomplicated principle and mathematical model,less parameters to be preset during running,better optimization ability and faster running speed.However,it also has a series of problems such as slow convergence speed,low global search accuracy and easy to be affected by local optimal solution.Aiming at the above problems of Gray Wolf algorithm,two improved GWO algorithms,ApDGWO and SSAGWO,are proposed in this paper,and they are used to solve the leakage source location problem of hazardous chemicals with joint probability distribution of wind speed and direction.The main contents of this paper are as follows:(1)This paper analyzes the research status of grey wolf optimizer at home and abroad,deeply studies the principle and mathematical model of Grey Wolf Optimizer,tests and analyzes the global optimization ability of the algorithm,and finally summarizes its limitations.(2)An improved Grey Wolf Optimizer(ApDGWO)based on assistant population strategy and decision disturbance strategy is proposed.In order to solve the shortcomings of standard gray wolf algorithm,such as the decrease of population diversity and poor global ergodic ability with the increase of algorithm cycle times,this paper adds a kind of assistance population on the basis of standard gray wolf algorithm;in order to improve the ability of gray wolf individual to jump out of local optimum,this paper introduces decision disturbance strategy on the basis of standard gray wolf algorithm.Through the standard test function simulation experiment,the ApDGWO algorithm has better performance.(3)A hybrid Grey Wolf Optimizer based on sparrow search quadratic optimization(SSAGWO)is proposed.In view of the deficiency that GWO generates initial population randomly may reduce the search performance of the algorithm,the good point set method is used to generate uniform initial grey wolf population in the search space,which is helpful for the algorithm to traverse the feasible solutions in the search space and improve the optimization efficiency.The Sparrow Search Algorithm is integrated with the Grey Wolf Optimizer,and the global search performance of Sparrow Search Algorithm is used Improve the performance of Grey Wolf Optimizer.Through the standard test function simulation experiment,it shows that SSAGWO algorithm has better performance.(4)According to the environmental factors such as wind speed and wind direction in the actual application environment,the Gaussian plume model is improved by using the joint distribution model of wind speed and wind direction.Two improved hybrid Grey Wolf Optimizers are applied to the back calculation of leakage source strength,so that the information of leakage source intensity can be obtained quickly and accurately in the case of hazardous chemicals leakage.The simulation results show that the method based on ApDGWO and SSAGWO has good positioning accuracy.