Improved Fruit Fly Optimization Algorithm And Its Applications

Fruit fly Optimization Algorithm(FOA)is a new kind of swarm intellingence optimization algorithm,which simulates foraging behavior of natural fruit fly,because of merits of its less parameters,simple process and high performance,et al,FOA has been applied widely in many industrial fields.However,it is difficult for FOA algorithm to avoid the inherent defects of swarm intelligence algorithm,such as premature convergence,easily relapsing into local extremum and poor stability.In addition,there are many optimization problems that need to be solved in practical applicaton,such as single peak,multi-peak,low dimensional and high dimensional,et al,so it is urgent to improve the basic FOA algorithm.The main research work and creative achievements are summarized as follows:(1)A Self-Adaptive Fruit fly Optimization Algorithm(SA-FOA)based on individual differences is proposedIn order to overcome the problems of FOA,such as easily relapsing into local extremum and unstable convergence resulted in the process of optimizing,A Self-Adaptive Fruit fly Optimization Algorithm(SA-FOA)based on the individual differences is proposed.This algorithm,through the iteration procedures,uses a control strategy of decreasing step size,the fruit in poor location had a relatively longer step,so that the algorithm could search the solution space more fully and enhance the search ability near global sides,dynamic balance is achieved effectively between global and local search,meanwhile,it enhances the optimization of the algorithm.The simulation verification and results of six complex test functions show that SA-FOA has the advantages of better global searching ability,much better in solving the high-dimensional of multi-peak function optimization problems.(2)A financial prediction model of Z-Score for listed companies based on improved FOA algorithm is proposedIn order to improve the prediction ability of the traditional Z-Score financial prediction model,this paper proposes a financial prediction model of Z-Score for listed companies based on improved FOA algorithm by combining the good searching ability of improved FOA algorithm and the Z-Score financial prediction model.The Root Mean Square Error(RMSE)between the predicted value and real value is reduced by improved FOA algorithm being applied to optimize the parameters of Z-Score model.We compare the predicted value and real value of the financial data of listed companies to test the accuracy of financial prediction.The experimental results are as follows: accuracies of the traditional Z-Score financial prediction model,FOA algorithm optimize the parameters of Z-Score model,and improved FOA algorithm optimize the parameters of Z-Score model are 65%,70%,and 80%,respectively.Experiments show that the improved algorithm significantly improves the predictive ability of Z-Score financial prediction model,it is also illustrated the validity of the algorithm.