融合多策略的鸟群算法及油层识别ELM模型优化

为了改进鸟群算法易陷入局部最优、收敛速度慢以及种群多样性不足的缺点,提出融合多策略的鸟群算法.引入混沌权重和对称切线混沌加速系数以及高斯扰动策略,增强算法跳出局部最优的能力;引入混合多步选择和自适应步长因子策略,加快算法的收敛速度;引入小波变异策略,丰富算法的种群多样性.实验采用10个基准测试函数,将改进的算法与另外5...

Mix with Multiple Strategies Bird Swarm Algorithm and Optimization of ELM Model in Oil Layer Classification

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.