为简化混合装配平衡问题的求解,进而提高装配线的生产效率,在兼顾产品切换引起负荷波动的基础上,综合工作站数、工作负荷平衡和任务关联度三个优化目标,提出一种求解多目标混合品种装配线平衡问题的改进型IWD(intelligent water drop)...为简化混合装配平衡问题的求解,进而提高装配线的生产效率,在兼顾产品切换引起负荷波动的基础上,综合工作站数、工作负荷平衡和任务关联度三个优化目标,提出一种求解多目标混合品种装配线平衡问题的改进型IWD(intelligent water drop)算法。对IWD算法的节点转移规则进行改进,加入最大概率引导规则和随机搜索规则;采用Pareto占优的方式对解进行分层以获得前沿解集,并根据分层结果给每个粒子提供一个启发值,依据启发值实施全局更新,增加算法的全局搜索能力;通过测试各种标准问题,验证了改进型IWD算法比遗传算法的求解速度更快、效率更高。展开更多
Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to f...Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.展开更多
文摘为简化混合装配平衡问题的求解,进而提高装配线的生产效率,在兼顾产品切换引起负荷波动的基础上,综合工作站数、工作负荷平衡和任务关联度三个优化目标,提出一种求解多目标混合品种装配线平衡问题的改进型IWD(intelligent water drop)算法。对IWD算法的节点转移规则进行改进,加入最大概率引导规则和随机搜索规则;采用Pareto占优的方式对解进行分层以获得前沿解集,并根据分层结果给每个粒子提供一个启发值,依据启发值实施全局更新,增加算法的全局搜索能力;通过测试各种标准问题,验证了改进型IWD算法比遗传算法的求解速度更快、效率更高。
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IF-PSAU-2021/01/18596).
文摘Aging is a natural process that leads to debility,disease,and dependency.Alzheimer’s disease(AD)causes degeneration of the brain cells leading to cognitive decline and memory loss,as well as dependence on others to fulfill basic daily needs.AD is the major cause of dementia.Computer-aided diagnosis(CADx)tools aid medical practitioners in accurately identifying diseases such as AD in patients.This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop(IWD)algorithm and the Random Forest(RF)classifier.The IWD algorithm an efficient feature selection method,was used to identify the most deterministic features of AD in the dataset.RF is an ensemble method that leverages multiple weak learners to classify a patient’s disease as either demented(DN)or cognitively normal(CN).The proposed tool also classifies patients as mild cognitive impairment(MCI)or CN.The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer’s Disease Neuroimaging Initiative(ADNI).The RF ensemble method achieves 100%accuracy in identifying DN patients from CN patients.The classification accuracy for classifying patients as MCI or CN is 92%.This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.
