Improvements And Applications Of Fruit Fly Optimization Algorithm

Fruit flies optimization algorithm(FOA)is a new type of intelligent optimization algorithm that is simulating flies the nature group looking for food behavior and phenomenon.There are simple,less control variables,easy to understand,easy to programming which is the Fruit flies optimization algorithm of the model,and the search performance of it is strong robustness.The algorithm is attentioned and researched by more and more scholar from home and abrosd,and it has been successfully applied in engineering and scientific computing,ect.But there are early convergence speed too fast,easy to fall into local optimal value.In the late of convergence algotithm is lack of population diversity lead to shortcomings such as slow convergence speed so that the application range of fruit flies optimization algorithm is limited greatly.Therefore,whar fruit flies optimization algorithm will be in the study is important theoretical significance and application prospects.This paper is aiming at some deficiencies of the fruit flies optimization algorithm.We improved the fruit flies optimization algorithm which is from the new strategy of optimization algorithm structure and coding mode.Then the algorithm is applied to functions optimization and engineering optimization.Its aim is to improve the exploratory performance of fruit flies optimization algorithm,perfect the algorithm's theoretical basis,expand the application range of the algorithm.This thesis mainly research result are as following:(1)Aim at shortcoming of fruit flies optimization algorithm which is search speed slow and is not high of optimal sccuracy.Take full advantage of the ideas which growth speed of exponential function is fast,Introduce the strategy of exponential growth into step length of fruit flies' individual explore.The algorithm is improved,so a new fruit fly optimization algorithm based on exponential function adaptive steps(EFOA)is put presented,so that it accelerate the algorithm convergence speed,enhance the global detection ability of the algorithm,effectively avoid flies to individual search prematurely into a local optimum and overcome the shortcoming of the searching precision is less than the late.The experimental results show that with improved of the optimization algorithm has significantly improved performance.(2)In order to increase the diversity of fruit flies optimization's population.The polar coordinate mothod is applied to the fruit flies optimization algorithm,so a fruit fly optimization algorithm based on polar coordinate coding is presented.Thought the polar code instead of the traditional binary encoding comfirm the method of solution.The individual diversity and scope of the search space is expanded,meanwhile it is weakening sensitivity of search on perception distance and step.The convergence and stability of the algorithm are improved effectively,so as the flies are caught in local optimum when individual search.The experiment results show the fruit fly optimization algorithm based on polar coordinate coding is provided with certain advantage in solving complex optimization problems.