Particle swarm optimization (PSO) method cannot be directly used in the problem of hyper-parameter estimation since the mathematical formulation of the mapping from hyper-parameters to loss function or generalization accuracy is unclear. Bayesian optimization (BO) framework is capable of converting the optimization of the hyper-parameters into the optimization of an acquisition function. The acquisition function is non-convex and multi-peak. So the problem can be better solved by the PSO. The proposed method in this paper uses the particle swarm method to optimize the acquisition function in the BO framework to get better hyper-parameters. The performances of proposed method in both of the classification and regression models are evaluated and demonstrated. The results on several benchmark problems are improved.

翻译：粒子群优化(PSO)方法不能直接用于超参数估计问题,因为从超参数绘图到丢失函数或一般化精确度的数学配方尚不清楚。 贝叶斯优化(BO)框架能够将超参数优化转化为获取功能的优化。 获取功能是非电解和多点的。 因此,问题可以由PSO更好地解决。 本文中的拟议方法使用粒子群优化BO框架中的获取功能以获得更好的超参数。 对分类和回归模型中的拟议方法的性能进行了评估和演示。 几个基准问题的结果得到了改进。