基于改进果蝇优化算法的随机森林回归模型及其在风速预测中的应用

针对随机森林(RF)算法在风速预测中存在参数选择困难及预测精度低的问题,提出了基于改进果蝇优化算法(IFOA)的随机森林回归(RFR)模型.在果蝇优化算法(FOA)中引入指数函数和三角函数实现搜索步长的自适应更新,增强全局寻优和局部探索的能力.结合RFR算法对噪声和异常值具有良好容忍度的优点,利用IFOA优化RFR主要参数,将优化后的模型应用于风速预测.实验结果表明,IFOA-RFR组合模型相比于其他模型具有更高的预测精度,验证了该方法在风速预测中的可行性.

Random forest regression model based on improved fruit fly optimization algorithm and its application in wind speed forecasting

To solve the problem that it is difficult to determine the combination of parameters and obtain the precise forecasting results for the wind speed forecasting based on random forest (RF), an improved fruit fly optimization algorithm (IFOA) was used to optimize the parameters of RFR. Exponential function and trigonometric function were introduced into the fruit fly optimization algorithm (FOA) to realize the adaptive update of step size in search, which enhances the algorithm’s ability of global optimization and local exploration. Combining the advantages of RFR with a good tolerance for noise and abnormal values, IFOA was used to optimize the main parameters of RFR, and the optimized model was applied to wind speed forecasting. The experimental results show that the IFOA-RFR combined model has higher prediction accuracy compared with other models, and the feasibility of this method in wind speed prediction is verified.