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<p>Blasting mean fragment size, as a key indicator of blasting effectiveness, directly impacts the subsequent mineral processing efficiency and economic benefits. This study aimed to develop a machine learning-based model for predicting blasting mean fragment size to improve the accuracy and efficiency of blasting design. This study fine-tuned the XGBoost model using five metaheuristic optimization algorithms: ant colony optimization (ACO), differential evolution (DE), inverse diffusion optimization (IWO), particle swarm optimization (PSO), and simulated annealing (SA). The results showed that ACO, DE, and PSO produced better optimization results. Based on this, the study further introduced generative adversarial networks (GAN), integrating them with the three best-performing hybrid models. The results showed that the XGBoost hybrid model combining DE and GAN had the optimal prediction performance. The model’s root mean square error, mean absolute error, coefficient of determination, and variance explanation values were 0.0137, 0.0109, 0.9513, and 95.30%, respectively. Ultimately, this study developed an XGBoost hybrid model for predicting blast fragmentation, integrating DE and GAN.</p>

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Blasting Mean Fragment Size Prediction Based on XGBoost and Metaheuristic Optimization Algorithms

  • 期刊论文

  • 发表日期：

    2025年6月14日

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  • Haitao Meng,
  • Ming Tao,
  • Rendong Huang,
  • Yan Zhao,
  • Yuanquan Xu

摘要

Blasting mean fragment size, as a key indicator of blasting effectiveness, directly impacts the subsequent mineral processing efficiency and economic benefits. This study aimed to develop a machine learning-based model for predicting blasting mean fragment size to improve the accuracy and efficiency of blasting design. This study fine-tuned the XGBoost model using five metaheuristic optimization algorithms: ant colony optimization (ACO), differential evolution (DE), inverse diffusion optimization (IWO), particle swarm optimization (PSO), and simulated annealing (SA). The results showed that ACO, DE, and PSO produced better optimization results. Based on this, the study further introduced generative adversarial networks (GAN), integrating them with the three best-performing hybrid models. The results showed that the XGBoost hybrid model combining DE and GAN had the optimal prediction performance. The model’s root mean square error, mean absolute error, coefficient of determination, and variance explanation values were 0.0137, 0.0109, 0.9513, and 95.30%, respectively. Ultimately, this study developed an XGBoost hybrid model for predicting blast fragmentation, integrating DE and GAN.