针对煤与瓦斯突出事故的复杂性以及数据获取困难导致预测准确率低的问题,提出基于密度的噪声应用空间聚类-改进哈里斯鹰优化-支持向量机(density based spatial clustering of applications with noise-improved Harris hawks optimizat...针对煤与瓦斯突出事故的复杂性以及数据获取困难导致预测准确率低的问题,提出基于密度的噪声应用空间聚类-改进哈里斯鹰优化-支持向量机(density based spatial clustering of applications with noise-improved Harris hawks optimization-support vector machine, DBSCAN-IHHO-SVM)预测模型。首先,选取瓦斯含量、瓦斯压力、煤层孔隙率、煤层坚固性系数作为预测指标,对数据中的缺失值采用均值填补处理,利用生成式对抗网络(generative adversarial network, GAN)扩充突出数据量。接着,采用DBSCAN从非突出数据中识别潜在危险数据,并将其作为新的突出数据。最后,引入IHHO调整SVM模型参数,将处理后的数据输入IHHO-SVM模型进行预测分析。结果表明,相比于原始SVM模型,DBSCAN-IHHO-SVM模型的整体预测准确率、危险数据识别率分别提升了5.87%、38.46%。在突出数据样本有限的情况下,DBSCAN-IHHO-SVM模型能有效挖掘非突出数据潜在信息,实现精准预警,为该领域研究提供了新思路。展开更多
This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lac...This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lack of thorough exploitation depth.To tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators.The effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test functions.Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities.Additionally,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional datasets.Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets.The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature selection.Furthermore,IHHO-FS shows strong competitiveness relative to numerous algorithms.展开更多
针对大规模可再生能源(renewable energy sources, RESs)接入电网后的间歇性和波动性,配电网向主动配电网(active distribution network, ADN)过渡资源配置的复杂化等问题,提出了基于改进哈里斯鹰优化(improved Harris hawk optimizatio...针对大规模可再生能源(renewable energy sources, RESs)接入电网后的间歇性和波动性,配电网向主动配电网(active distribution network, ADN)过渡资源配置的复杂化等问题,提出了基于改进哈里斯鹰优化(improved Harris hawk optimization, IHHO)算法的主动配电网最优规划模型。首先,对风光出力和负荷需求的典型场景进行构建;建立考虑系统电压增强指数、有功和无功损耗指标在内的最优规划模型;采用IHHO算法进行优化求解;最后,以IEEE57节点系统为例对所提出的模型和方法进行了仿真分析,验证了模型和方法的有效性和先进性。展开更多
基金supported by the National Natural Science Foundation of China(grant number 62073330)constituted a segment of a project associated with the School of Computer Science and Information Engineering at Harbin Normal University。
文摘This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lack of thorough exploitation depth.To tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators.The effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test functions.Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities.Additionally,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional datasets.Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets.The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature selection.Furthermore,IHHO-FS shows strong competitiveness relative to numerous algorithms.
