基于混合改进GSO与GRNN并行集成学习模型

Parallel Ensemble Learning Model Based on Hybrid Improved GSO and GRNN

针对萤火虫群优化算法(GSO)不稳定、收敛速度较慢与收敛精度较低等问题和广义回归神经网络(GRNN)的网络结构导致预测误差的特性,提出基于混合改进萤火虫群算法与广义回归神经网络并行集成学习模型,应用于雾霾预测.首先构建融合多种搜索策略的混合改进萤火虫群优化算法(HIGSO),并使用标准测试函数验证算法性能.然后结合HIGSO与引入扰动因子的GRNN模型,建立并行集成学习模型,并通过UCI标准数据集验证模型的有效性与可行性.最后将模型应用于北京、上海和广州地区的雾霾预测,进一步验证模型在雾霾预测中的性能.

Glowworm swarm optimization(GSO) has problems of instability and slow convergence speed and accuracy. The error of general regression neural network(GRNN) is easily caused by the network structure. Aiming at these defects, a parallel ensemble learning model based on hybrid improved GSO and GRNN is proposed, and it is applied for haze prediction. Firstly, a hybrid improved glowworm swarm optimization(HIGSO) algorithm is constructed fusing multiple search strategies. The performance of the algorithm is verified via standard test functions. Next, the HIGSO algorithm is integrated with GRNN with disturbance parameter to construct a parallel ensemble learning model, and its validity and feasibility are verified on UCI standard dataset. Finally, the proposed model is applied for haze prediction in Beijing, Shanghai and Guangzhou areas to further verify its performance in haze prediction.