Research And Application Of Biogeographic Optimization Algorithm

Biogeography-based optimization(BBO)is a new evolutionary algorithm proposed in recent years.It is widely applied in numericalcalculation,economic forecasting,and environmental monitoring.It is an important direction of research at home and abroad.This algorithm is an optimization algorithm which simulates the evolution law of nature.Compared with the traditional optimization algorithms,BBO has less requirements on the continuous and differentiable properties of the optimization function,the structure of the algorithm is simple and easy to implement,and is not divisible by multiple peaks.Moreover,it performs well in the optimization of complex functions such as combinatorial optimization and constrained optimization.In this paper,on the basis of analyzing the basic principle of BBO algorithm,in view of the biogeography species migration,optimization algorithm with the diversity and particularity of behavior such as variation,including a variety of integrated application,improved BBO algorithm is given,and combined with BP neural network algorithm,the main research work includes:(1)Correct the migration operator in dynamic mode and give an improved DEBBO algorithm.The DEBBO algorithm introduces a dynamic factor F to dynamically adjust the migration operator of the algorithm and increase the search space of the algorithm.And in random value,using the cluster analysis method of random Numbers:clustering operation,selection of data as the basis of each iteration DEBBO algorithm,numerical experiment results show that the improved algorithm has better accuracy.(2)Solve the location problem of emergency service facilities with the improved DEBBO algorithm.The algorithm starts from the specific situation of the emergency point,establishes a model that takes time satisfaction as the goal,and compares it with other algorithms for errors and timeliness.The improved DEBBO algorithm is used to solve the location problem of emergency service facilities.Based on the specific situation of the emergency point,the algorithm set up a model with time satisfaction as the target,and compared the error and aging with other algorithms.(3)The BBO algorithm is used to optimize the initial weights and thresholds of BP neural network,and the BP neural network algorithm based on BBO algorithm is proposed.For the problems that BP neural network is easy to fall into local minimum and the convergence speed is slow,the BBO algorithm is used to process the initial process and improve the overall processing ability of BP neural network.Selection of city house prices in recent years and its main influencing factors as the experimental data,respectively,using the traditional BP neural network and after BBO algorithm to optimize the BP neural network for training and simulation experiment,the results show that after BBO algorithm to optimize the BP neural network prediction model can speed up the convergence speed of the neural network,improve the prediction accuracy of the price of the house.