Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
为解决电力系统稳定器(power system stabilizer,PSS)参数设置困难及传统的智能优化算法在寻优的过程中容易陷入局部最优,导致收敛速度下降的问题,因此文中采用混合策略改进SSA算法优化PSS参数。首先采用tent混沌映射来优化初始种群,提...为解决电力系统稳定器(power system stabilizer,PSS)参数设置困难及传统的智能优化算法在寻优的过程中容易陷入局部最优,导致收敛速度下降的问题,因此文中采用混合策略改进SSA算法优化PSS参数。首先采用tent混沌映射来优化初始种群,提高种群多样性;采用柯西变异、正余弦策略和反向学习策略(opposition-based learning,OBL)来提升收敛速度;然后对6种测试函数进行寻优实验,将改进的SSA与PSO、GWO、SSA、SSSA优化结果对比,验证改进的SSA算法具有更好的收敛速度和稳定性;最后,将改进的SSA算法应用在单机无穷大系统及四机二区域系统的PSS参数优化中,与其他算法优化结果比较,验证改进的SSA在PSS参数优化方面的鲁棒性更好,收敛速度更快。展开更多
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
文摘为解决电力系统稳定器(power system stabilizer,PSS)参数设置困难及传统的智能优化算法在寻优的过程中容易陷入局部最优,导致收敛速度下降的问题,因此文中采用混合策略改进SSA算法优化PSS参数。首先采用tent混沌映射来优化初始种群,提高种群多样性;采用柯西变异、正余弦策略和反向学习策略(opposition-based learning,OBL)来提升收敛速度;然后对6种测试函数进行寻优实验,将改进的SSA与PSO、GWO、SSA、SSSA优化结果对比,验证改进的SSA算法具有更好的收敛速度和稳定性;最后,将改进的SSA算法应用在单机无穷大系统及四机二区域系统的PSS参数优化中,与其他算法优化结果比较,验证改进的SSA在PSS参数优化方面的鲁棒性更好,收敛速度更快。
