隧道爆破参数不当会严重影响隧道施工的安全和质量。因此，确定合适的爆破参数是隧道施工中一项重要的工作。为了解决此问题，基于深度学习模型鲸鱼优化深度置信网络（whale optimization deep belief network, WO-DBN）及多目标优化算法非支配排序遗传算法II（non-dominated sorting genetic algorithm II, NSGA-II），提出了一种进行隧道爆破参数优化的智能算法。首先，使用开发的深度学习模型WO-DBN构建了基于地质参数及爆破参数进行隧道爆破施工安全及质量预测的智能模型，以隧道拱顶下沉和超欠挖面积作为施工安全及质量评价的指标。其次，基于建立的隧道爆破施工安全及质量评价模型，采用NSGA-II以控制拱顶下沉和超欠挖面积为目标，提出进行隧道爆破参数优化的智能算法。最后，以蟠龙山公路隧道爆破施工为例，对提出的新算法进行工程应用验证。结果表明，采用新算法得到的施工参数，可以使得隧道拱顶下沉和超欠挖面积分别降低27.05%和60.30%，施工效果得到极大提高。因此，提出的智能算法可以为隧道爆破参数的实时优化控制提供技术支持，为隧道施工的顺利进行提供有力保障。

Improper tunnel blasting parameters will seriously affect the safety and quality of tunnel construction. Therefore, the determination of appropriate blasting parameters is an important work in tunnel construction. In order to solve this problem, based on deep learning model-whale optimization deep belief network (WO-DBN) and multi-objective optimization algorithm-non-dominated sorting genetic algorithm II (NSGA-II), an intelligent algorithm for tunnel blasting parameters optimization was proposed. Firstly, using the developed deep learning model WO-DBN, an intelligent model for predicting the safety and quality of tunnel blasting construction based on geological parameters and blasting parameters was constructed. The tunnel crown subsidence and overbreak and underbreak area were taken as the index of construction safety and quality evaluation. Secondly, based on the established tunnel blasting construction safety and quality evaluation model, an intelligent algorithm for tunnel blasting parameter optimization was proposed by using NSGA-II to control crown subsidence, overbreak and underbreak area. Finally, taking the blasting construction of Panlongshan highway tunnel as an example, the proposed new algorithm was verified by engineering application. The results show that the construction parameters obtained by the new algorithm can reduce the tunnel crown subsidence and the overbreak and underbreak area by 27.05% and 60.30%, respectively, and the construction effect is greatly improved. Therefore, the proposed intelligent algorithm can provide technical support for the real-time optimization control of tunnel blasting parameters and provide a strong guarantee for the smooth progress of tunnel construction.