Improved Artificial Bee Colony Algorithm And Its Application In Prediction Of Urban Land Use

The swarm intelligence optimization algorithm belongs to a kind of stochastic optimization algorithms and has a good performance for solving the complex optimization problem,thus it has attracted the attention of many experts and scholars.Artificial Bee Colony(ABC)algorithm is an emerging swarm intelligence optimization algorithm which mainly simulates the behavioral mechanism of bee swarm in nature to obtain the optimal solution for the optimized problem.However,the algorithm also has certain problems,such as premature convergence,slow convergence rate,poor local search capability,low precision,optimization effects depends on the parameters and insufficient depth of theoretical analysis,and so on.In order to improve the efficiency and accuracy of ABC algorithm in processing optimization problems,a Hybrid Artificial Bee Colony algorithm based on Mutation of inferior solutions(MHABC)is proposed in this thesis.Two improved strategies,mutation of inferior solutions and binary crossover operation,were adopted in the MHABC algorithm.Simulation experiments results with multiple standard test functions showed that the improved algorithm has better performance of in-depth search,can effectively improve the convergence speed and accuracy of the solution,and can also obtain good results in terms of stability.The issue of land use change is an important issue related to urban land use planning,ecological environment protection and sustainable development.Land use change directly reflects the development of human society and economy and is an important research direction for environmental change and sustainable development in China.The prediction of land use change not only has an important impact on the land use pattern,but also has a profound impact on the ecological environment change and socio-economic development.Therefore,based on the improved artificial bee colony algorithm and the data interval model based on cellular automata,this paper constructs an optimized prediction algorithm based on improved artificial bee colony algorithm for predicting land use change.Dengzhou City,Henan Province,the third phase of remote sensing data for the experimental data to demonstrate the case,verifying the effectiveness of the algorithm.In summary,this paper introduces two improved strategies of bad mutation and binary crossover operation to improve the basic artificial bee colony algorithm,and combines the optimized prediction algorithm based on the improved artificial bee colony algorithm.The experimental demonstration has been partially selected.The urban road network data are training objects and used to study the prediction of urban land use type changes.In the example verification section,remote sensing data from Dengzhou City in 2005,2010,and 2015 were obtained.Simulation experiments were conducted on the dynamic expansion of Dengzhou City.The experimental results proved the validity of the algorithm.