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
Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to ...Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).展开更多
Optical networks act as a backbone for coming generation high speed applications.These applications demand a very high bandwidth which can be exploited with the use of wavelength division multiplexing(WDM)technology.T...Optical networks act as a backbone for coming generation high speed applications.These applications demand a very high bandwidth which can be exploited with the use of wavelength division multiplexing(WDM)technology.The issue of setting light paths for the traffic demands is routing and wavelength assignment(RWA)problem.Based on the type of traffic patterns,it can be categorized as offline or online RWA.In this paper,an effective solution to offline(static)routing and wavelength assignment is presented considering multiple objectives simultaneously.Initially,the flower pollination(FP)technique is utilized.Then the problem is extended with the parallel hybrid technique with flower pollination and intelligent water drop algorithm(FPIWDA).Further,FPIWD is hybrid in parallel with simulated annealing(SA)algorithm to propose a parallel hybrid algorithm FPIWDSA.The results obtained through extensive simulation show the superiority of FPIWD as compared to FP.Moreover,the results in terms of blocking probability with respect to wavelengths and load of FPIWDSA are more propitious than FP and FPIWD.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a grant from the“Research Center of the Female Scientific and Medical Colleges”,the Deanship of Scientific Research,King Saud University.
文摘Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).
文摘Optical networks act as a backbone for coming generation high speed applications.These applications demand a very high bandwidth which can be exploited with the use of wavelength division multiplexing(WDM)technology.The issue of setting light paths for the traffic demands is routing and wavelength assignment(RWA)problem.Based on the type of traffic patterns,it can be categorized as offline or online RWA.In this paper,an effective solution to offline(static)routing and wavelength assignment is presented considering multiple objectives simultaneously.Initially,the flower pollination(FP)technique is utilized.Then the problem is extended with the parallel hybrid technique with flower pollination and intelligent water drop algorithm(FPIWDA).Further,FPIWD is hybrid in parallel with simulated annealing(SA)algorithm to propose a parallel hybrid algorithm FPIWDSA.The results obtained through extensive simulation show the superiority of FPIWD as compared to FP.Moreover,the results in terms of blocking probability with respect to wavelengths and load of FPIWDSA are more propitious than FP and FPIWD.
