In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为...针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为降低专家的主观偏差,应用基于层级的模糊权重评估(Fuzzy level based weight assessment,FLBWA)法来计算各评价指标权重;继而结合改进的Borda-全乘比例多目标优化(Borda-multi-objective optimization on the basis of ratio analysis plus full multiplicative form,Borda-MULTIMOORA)法计算开发适宜性指数,从而能够更加准确、高效地得到评价结果;之后,基于灰狼优化算法的反向传播(Grey wolf optimizer with back propagation,GWO-BP)神经网络构建并训练智能模型,将适宜性分析转化为自动化、高效化和智能化的过程;最后,以山东省风浪联合开发区划为例验证该模型的可行性和合理性。根据实例验证,该模型可以实现风浪联合开发区划的智能化,为相关领域的研究和政府规划提供参考。展开更多
飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器,使其具有监测其本身状态和损伤的能力,其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因,传统的反演算法精度不高。为...飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器,使其具有监测其本身状态和损伤的能力,其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因,传统的反演算法精度不高。为了提高智能蒙皮的监测精度,提出一种将反向传播(back propagation,BP)神经网络与改进的灰狼优化算法(improved grey wolf optimizer,IGWO)优化克里金模型融合的BP-IGWO反演算法。制作智能蒙皮原理样件,通过风洞试验对该算法进行验证。结果表明:BP-IGWO反演算法较之传统反演算法具有更高的精度和细节呈现能力,能更好地监测智能蒙皮的状态。展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
针对灰狼优化(grey wolf optimization, GWO)算法易陷入局部最优和收敛精度差的问题,提出了一种基于对立搜索和Levy飞行策略的改进灰狼优化算法——OLGWO算法.在算法初始化阶段,采用对立搜索策略以缩小可行解范围;在灰狼位置更新过程中...针对灰狼优化(grey wolf optimization, GWO)算法易陷入局部最优和收敛精度差的问题,提出了一种基于对立搜索和Levy飞行策略的改进灰狼优化算法——OLGWO算法.在算法初始化阶段,采用对立搜索策略以缩小可行解范围;在灰狼位置更新过程中,为避免算法陷入局部最优采用了Levy飞行策略. 4个标准测试函数的仿真实验表明,所提OLGWO算法在收敛速度及求解精度方面均优于GWO算法,可以较快且准确地搜索到目标函数的最优值.基于OLGWO算法对隧道射线跟踪传播模型进行校正的结果表明,校正后的模型在均方根误差和线性相关性方面具有较优的性能,能够实现铁路隧道环境中信号接收功率的精确预测.展开更多
针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;...针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。展开更多
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
文摘针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为降低专家的主观偏差,应用基于层级的模糊权重评估(Fuzzy level based weight assessment,FLBWA)法来计算各评价指标权重;继而结合改进的Borda-全乘比例多目标优化(Borda-multi-objective optimization on the basis of ratio analysis plus full multiplicative form,Borda-MULTIMOORA)法计算开发适宜性指数,从而能够更加准确、高效地得到评价结果;之后,基于灰狼优化算法的反向传播(Grey wolf optimizer with back propagation,GWO-BP)神经网络构建并训练智能模型,将适宜性分析转化为自动化、高效化和智能化的过程;最后,以山东省风浪联合开发区划为例验证该模型的可行性和合理性。根据实例验证,该模型可以实现风浪联合开发区划的智能化,为相关领域的研究和政府规划提供参考。
文摘飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器,使其具有监测其本身状态和损伤的能力,其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因,传统的反演算法精度不高。为了提高智能蒙皮的监测精度,提出一种将反向传播(back propagation,BP)神经网络与改进的灰狼优化算法(improved grey wolf optimizer,IGWO)优化克里金模型融合的BP-IGWO反演算法。制作智能蒙皮原理样件,通过风洞试验对该算法进行验证。结果表明:BP-IGWO反演算法较之传统反演算法具有更高的精度和细节呈现能力,能更好地监测智能蒙皮的状态。
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
文摘针对灰狼优化(grey wolf optimization, GWO)算法易陷入局部最优和收敛精度差的问题,提出了一种基于对立搜索和Levy飞行策略的改进灰狼优化算法——OLGWO算法.在算法初始化阶段,采用对立搜索策略以缩小可行解范围;在灰狼位置更新过程中,为避免算法陷入局部最优采用了Levy飞行策略. 4个标准测试函数的仿真实验表明,所提OLGWO算法在收敛速度及求解精度方面均优于GWO算法,可以较快且准确地搜索到目标函数的最优值.基于OLGWO算法对隧道射线跟踪传播模型进行校正的结果表明,校正后的模型在均方根误差和线性相关性方面具有较优的性能,能够实现铁路隧道环境中信号接收功率的精确预测.
文摘针对特征权重难以准确量化的问题,提出一种基于灰狼优化(grey wolf optimizer, GWO)算法和鸟群算法(bird swarm algorithm, BSA)的混合算法,用于特征权重的寻优。首先,将Chebyshev映射、反向学习与精英策略用于混合算法的初始种群生成;其次,将改进后的GWO算法位置更新策略融入BSA的觅食行为中,得到一种新的局部搜索策略;然后,将BSA的警觉行为与飞行行为用作混合算法的全局搜索平衡策略,从而得到一种收敛的灰狼-鸟群算法(grey wolf and bird swarm algorithm, GWBSA),通过GWBSA的迭代寻优可获得各特征的权重值。利用标准测试函数和标准分类数据集进行了对比实验,与遗传算法、蚁狮算法等方法相比,GWBSA具有较快的收敛速度且不易陷入局部最优,可以提高模式分类问题的求解质量。
