The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of c...The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.展开更多
针对传统的离职预测算法应用于现实中高维度小样本不平衡数据集时存在准确度低、易过拟合、鲁棒性差等问题,应用梯度增强集成分类器CatBoost算法,进行类别型特征处理,利用BOHB(Bayesian Optimization and Hyperband)寻找最优参数,结合...针对传统的离职预测算法应用于现实中高维度小样本不平衡数据集时存在准确度低、易过拟合、鲁棒性差等问题,应用梯度增强集成分类器CatBoost算法,进行类别型特征处理,利用BOHB(Bayesian Optimization and Hyperband)寻找最优参数,结合交叉验证对模型分类性能进行评估,建立分类模型并对护士离职情况进行预测。该算法采用高维小样本不平衡特征的上海部分公立医院的护士离职数据集,并与XGBoost、随机森林、支持向量机进行对比。实验结果表明,该算法准确度高,鲁棒性强,能够有效地对护士离职进行预测。展开更多
Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design...Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider(WS),grey wolf(GW),and brain storm optimization(BSO).The structural elements of the domes are treated as design variables collected in member groups.The structural stress and stability limitations are enforced by ASD-AISC provisions.Also,the displacement restrictions are considered in design procedure.The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface(OAPI).The optimum spatial steel dome designs achieved by proposed WS,GW,and BSO algorithms are compared with respect to solution accuracy,convergence rates,and reliability,utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization(HBO)algorithm.展开更多
基金National Natural Science Foundation of China with the Grant Number 61877045。
文摘The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune system.Too many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of DCA.The loss function of DCA is ambiguous due to its complexity.To reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter combination.However,these studies are either time-consuming or need to be revised in the case of non-convex functions.To overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss function.This study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion process.The BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential configuration.The experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.
文摘针对传统的离职预测算法应用于现实中高维度小样本不平衡数据集时存在准确度低、易过拟合、鲁棒性差等问题,应用梯度增强集成分类器CatBoost算法,进行类别型特征处理,利用BOHB(Bayesian Optimization and Hyperband)寻找最优参数,结合交叉验证对模型分类性能进行评估,建立分类模型并对护士离职情况进行预测。该算法采用高维小样本不平衡特征的上海部分公立医院的护士离职数据集,并与XGBoost、随机森林、支持向量机进行对比。实验结果表明,该算法准确度高,鲁棒性强,能够有效地对护士离职进行预测。
文摘Steel dome structures,with their striking structural forms,take a place among the impressive and aesthetic load bearing systems featuring large internal spaces without internal columns.In this paper,the seismic design optimization of spatial steel dome structures is achieved through three recent metaheuristic algorithms that are water strider(WS),grey wolf(GW),and brain storm optimization(BSO).The structural elements of the domes are treated as design variables collected in member groups.The structural stress and stability limitations are enforced by ASD-AISC provisions.Also,the displacement restrictions are considered in design procedure.The metaheuristic algorithms are encoded in MATLAB interacting with SAP2000 for gathering structural reactions through open application programming interface(OAPI).The optimum spatial steel dome designs achieved by proposed WS,GW,and BSO algorithms are compared with respect to solution accuracy,convergence rates,and reliability,utilizing three real-size design examples for considering both the previously reported optimum design results obtained by classical metaheuristic algorithms and a gradient descent-based hyperband optimization(HBO)algorithm.
