秃鹰搜索算法的优化及图像分类应用

针对秃鹰搜索算法(BES)存在求解的稳定性差且准确性低, 鲁棒性差等缺点, 提出了一种基于秃鹰搜索算法的新型算法(NBES). 首先, 在BES算法的选择搜索空间阶段融合正余弦优化机制算法, 构建融合后的位置更新公式. 其次, 在BES算法的搜索空间猎物阶段加入惯性权重自适应位置更新策略. 最后, 在BES算法俯冲阶段融合萤火虫优化机制算法, 重新定义位置更新公式. 通过11个标准测试函数验证NBES算法性能, 实验表明, NBES算法寻优准确性、收敛速度、鲁棒性都优于BES算法. 为了验证新算法的实际应用价值, 利用NBES算法优化卷积神经网络(CNN)中的超参数学习率, 并将优化后的图像分类模型用于医学影像病理性分类预测, 实验表明, 经过优化的CNN模型分类精度提高9%.

Optimization of Bald Eagle Search Algorithm and Its Application in Image Classification

A new algorithm based on the bald eagle search algorithm (NBES) is proposed to address the drawbacks of poor stability and low accuracy of the solution and poor robustness of the bald eagle search (BES) algorithm. First, the sine cosine optimization mechanism algorithm is fused in the search space selection stage of the BES algorithm, and the fused position update formula is constructed. Secondly, the inertial weight adaptive position update strategy is added in the search space prey phase of the BES algorithm. Finally, the position update formula is redefined by fusing the firefly optimization mechanism algorithm in the swoop phase of the BES algorithm. The performance of the NBES algorithm is verified by 11 standard test functions, and the experiments show that the NBES algorithm outperforms the BES algorithm in terms of search accuracy, convergence speed, and robustness. To verify the practical application value of the new algorithm, the hyperparameter learning rate in the convolutional neural network (CNN) is optimized by using the NBES algorithm, and the optimized image classification model is used in medical image pathology classification prediction, and the experiments show that the classification accuracy of the optimized CNN model is improved by 9%.