蝴蝶优化算法对大青杨生长速率预测模型的改进

为提高大青杨生长速率的预测精度，提出一种基于改进的蝴蝶优化算法（Improved Butterfly optimization algorithm，IBOA）与径向基函数（Radial basis function，RBF）神经网络结合的预测木材材性方法。通过使用佳点集法对标准蝴蝶算法中的种群进行初始化，将自适应切换频率和Levy飞行相结合进一步优化人工蝴蝶算法。构建出了新的IBOA-RBF神经网络木材材性预测模型，将得到的结果与其他几种算法优化的RBF神经网络预测结果进行对比。结果表明：基于IBOA-RBF神经网络模型预测效果最好，收敛速度从37步降低到了23步，预测结果误差达到了5.72%，预测精度最高。可见，对蝴蝶算法的改进是可行的，且对相关人员定向培养大青杨起到较大的帮助。

Prediction Model of Growth Rate of Populus ussuriensis Based on Improved Butterfly Optimization Algorithm

In order to improve the prediction accuracy of the growth rate of populus ussuriensis, a method for predicting wood properties based on the combination of improved butterfly optimization algorithm (IBOA) and radial basis function (RBF) neural network was proposed. The population in the standard butterfly algorithm is initialized by using the good point set method, and the artificial butterfly algorithm is further optimized by combining the adaptive switching frequency and Levy flight. A new prediction model of wood properties based on IBOA-RBF neural network was constructed, and the results were compared with those of RBF neural network optimized by other algorithms. The results show that the prediction effect based on IBOA-RBF neural network model is the best, the convergence speed is reduced from 37 steps to 23 steps, the prediction result error reaches 5.72%, and the prediction accuracy is the highest. It is concluded that the improvement of the butterfly algorithm is feasible, and plays a great role in the targeted cultivation of populus ussuriensis by relevant personnel.