## 1 Introduction  
Blasting is the preferred technique for open-pit mining activities, primarily aimed at achieving rock fragmentation. However, the release of blast energy will have adverse effects, e.g., seismic waves, flying rocks, and dust [1]. Hence, pre-blast prediction and optimization are essential tools to control blasting effects. In addition, research on blasting effects is shifting from traditional empirical formulas to intelligent algorithmic predictions. To predict and optimize blasting effectiveness, researchers have conducted many studies. The traditional prediction methods are based on empirical models. [PERSON] et al. [2] modified the Kuz-Ram empirical model to improve the accuracy of blasting block prediction. [PERSON] et al. [3] compared the performance of the Sadovsky formulation with other empirical models in predicting blast vibration. Recently, intelligent algorithms have gained widespread use in predicting blast-induced outcomes, including the predicting of adverse effects, e.g., backbreak [4], dust emissions [5], and vibration [6], the predicting of direct blasting results, e.g., block degree [7] and throwing distance [8]. Moreover, blasting impacts the operations of various production segments in open-pit mines [9]. For example, bulk ore produced by blasting implies additional loading, transportation, and crushing costs [10]. In addition, blast cracking and bulkiness affect crushing and grinding [11]. Therefore,various indirect effects can be predicted based on blasting, such as the prediction of shovel loading time, truck payload [12], production energy consumption [13], etc. Above all, the objects of prediction and optimization for blasting effectiveness are multifaceted [14]. As the research of intelligent algorithms in mining and natural resources is intensifying, the prediction and optimization of multiple objects by intelligent algorithms are receiving more attention [15; 16; 17]. The different contributions of explosive energy are essentially responsible for the diversity of blasting results, i.e., a smaller fraction of energy (about 20-30%) is used for rock fragmentation and removing, whereas most of the energy is wasted in various adverse effects [18]. Among the various adverse effects, blasting-induced ground vibrations are considered the most harmful [19], accounting for approximately 40% of the explosive energy [20]. For example, blasting vibrations affect crushers and explosives magazines in open-pit mines [21]. Therefore, when there are important buildings and unstable terrain around the blasting area, blasting-induced ground vibration should be strictly controlled [22]. Peak particle velocity (PPV) is generally considered a crucial indicator of blast-induced ground vibration intensity [23]. Over time, numerous empirical models have been developed and proven effective in predicting PPV [3; 24]. However, a literature review found that empirical models are less accurate [25; 26]. Therefore, adopting intelligent algorithms and incorporating more factors affecting PPV into prediction models improves prediction accuracy [27; 28; 29]. Scholars have paid significant attention in recent years to enhancing intelligent algorithms for accurate PPV prediction. For example, [PERSON] and [PERSON] [20] used the cuckoo optimization algorithm (COA) to optimize gene expression programming (GEP), and [PERSON] et al. [30] optimized XGBoost using the particle swarm algorithm (PSO). Therefore, the hybrid models predict PPV more effectively, e.g., by optimizing the parameter configuration of prediction models. In addition, preprocessing the input data using algorithms (e.g., clustering techniques and feature selection) can enable predictive models to exhibit a dominant performance in a comprehensive comparison [19; 31; 32]. Comparing the hybrid model with a single model, some scholars found a significant improvement in PPV prediction accuracy with the hybrid approach [33].