%0 Journal Article
%A SONG Fei
%A XIA Kewen
%A YANG Wenbiao
%T Mix with Multiple Strategies Bird Swarm Algorithm and Optimization of ELM Model in Oil Layer Classification
%D 2022
%R 10.3778/j.issn.1002-8331.2011-0378
%J Computer Engineering and Applications
%P 279-287
%V 58
%N 9
%X In order to improve the shortcomings of bird swarm algorithm, such as easy to fall into local optimum, slow convergence speed and insufficient diversity of population, the mix with multiple strategies bird swarm algorithm is proposed. The chaos weight, symmetric tangent chaos acceleration coefficient and Gaussian disturbance strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum. The hybrid multi-step selection and adaptive step size factor strategy are introduced to accelerate the convergence speed of the algorithm. The wavelet mutation strategy is introduced to increase the population diversity of the algorithm. 10?benchmark functions are used in the experiment, and the improved algorithm is compared with the other five intelligent algorithms, and the performance of the improved algorithm is verified to be better than other algorithms. In addition, in order to improve the accuracy of extreme learning machine（ELM） model in oil layer classification, the improved bird swarm algorithm is used to optimize the parameters of ELM model. The actual logging application shows that the ELM model optimized by improved bird colony algorithm has a significant effect in reservoir identification, which is better than ELM model optimized by genetic algorithm, particle swarm optimization algorithm and ant colony algorithm.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2011-0378