The iteration times and learning efficiency of kernel incremental extreme learning machines are always affected by the redundant nodes. A hybrid deep kernel incremental extreme learning machine (DKIELM) based on the improved coyote and beetle swarm optimization methods was proposed in this paper. A hybrid intelligent optimization algorithm based on the improved coyote optimization algorithm (ICOA) and improved beetle swarm optimization algorithm (IBSOA) was proposed to optimize the parameters and determine the number of effectively hidden layer neurons for the proposed DKIELM. A Gaussian global best-growing operator was adopted to replace the original growing operator in the intelligent optimization algorithm to improve COA searching efficiency and convergence. In the meantime, IBSOA was designed based on tent mapping inverse learning and dynamic mutation strategies to avoid falling into a local optimum. The experimental results demonstrated the feasibility and effectiveness of the proposed DKIELM with encouraging performances compared with other ELMs.

核增量极限学习机的迭代次数和学习效率总是受到冗余节点的影响。本文提出了一种基于改进的土狼和甲虫群优化方法的混合深度核增量极限学习机（DKIELM）。提出了一种基于改进土狼优化算法（ICOA）和改进甲虫群优化算法（IBSOA）的混合智能优化算法，以优化所提出的DKIELM的参数并确定有效隐藏层神经元的数量。智能优化算法中采用高斯全局最佳增长算子代替原有的增长算子，提高COA搜索效率和收敛性。同时，IBSOA 是基于帐篷映射逆向学习和动态变异策略设计的，以避免陷入局部最优。实验结果证明了所提出的 DKIELM 的可行性和有效性，与其他 ELM 相比，具有令人鼓舞的性能。