Cloud computing has been the core infrastructure for providing services to the offloaded workloads from IoT devices.However,for time-sensitive tasks,reducing end-to-end delay is a major concern.With advancements in th...Cloud computing has been the core infrastructure for providing services to the offloaded workloads from IoT devices.However,for time-sensitive tasks,reducing end-to-end delay is a major concern.With advancements in the IoT industry,the computation requirements of incoming tasks at the cloud are escalating,resulting in compromised quality of service.Fog computing emerged to alleviate such issues.However,the resources at the fog layer are limited and require efficient usage.The Whale Optimization Algorithm is a promising meta-heuristic algorithm extensively used to solve various optimization problems.However,being an exploitation-driven technique,its exploration potential is limited,resulting in reduced solution diversity,local optima,and poor convergence.To address these issues,this study proposes a dynamic opposition learning approach to enhance the Whale Optimization Algorithm to offload independent tasks.Opposition-Based Learning(OBL)has been extensively used to improve the exploration capability of the Whale Optimization Algorithm.However,it is computationally expensive and requires efficient utilization of appropriate OBL strategies to fully realize its advantages.Therefore,our proposed algorithm employs three OBL strategies at different stages to minimize end-to-end delay and improve load balancing during task offloading.First,basic OBL and quasi-OBL are employed during population initialization.Then,the proposed dynamic partial-opposition method enhances search space exploration using an information-based triggering mechanism that tracks the status of each agent.The results illustrate significant performance improvements by the proposed algorithm compared to SACO,PSOGA,IPSO,and oppoCWOA using the NASA Ames iPSC and HPC2N workload datasets.展开更多
6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,faul...6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.展开更多
Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design...Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.展开更多
基金supported and funded by‘Data Analytics and Visualization Development System for Subsurface Co2 Storage and Fluid Production’,Cost centre(015MD0-166)under the Center for research in Data Science(CerDaS)Universiti Teknologi PETRONAS,Malaysia.
文摘Cloud computing has been the core infrastructure for providing services to the offloaded workloads from IoT devices.However,for time-sensitive tasks,reducing end-to-end delay is a major concern.With advancements in the IoT industry,the computation requirements of incoming tasks at the cloud are escalating,resulting in compromised quality of service.Fog computing emerged to alleviate such issues.However,the resources at the fog layer are limited and require efficient usage.The Whale Optimization Algorithm is a promising meta-heuristic algorithm extensively used to solve various optimization problems.However,being an exploitation-driven technique,its exploration potential is limited,resulting in reduced solution diversity,local optima,and poor convergence.To address these issues,this study proposes a dynamic opposition learning approach to enhance the Whale Optimization Algorithm to offload independent tasks.Opposition-Based Learning(OBL)has been extensively used to improve the exploration capability of the Whale Optimization Algorithm.However,it is computationally expensive and requires efficient utilization of appropriate OBL strategies to fully realize its advantages.Therefore,our proposed algorithm employs three OBL strategies at different stages to minimize end-to-end delay and improve load balancing during task offloading.First,basic OBL and quasi-OBL are employed during population initialization.Then,the proposed dynamic partial-opposition method enhances search space exploration using an information-based triggering mechanism that tracks the status of each agent.The results illustrate significant performance improvements by the proposed algorithm compared to SACO,PSOGA,IPSO,and oppoCWOA using the NASA Ames iPSC and HPC2N workload datasets.
基金supported in part by the National Key Research and Development Project under Grant 2020YFB1806805partially funded through a grant from Qualcomm。
文摘6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.
文摘Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.
文摘教与学优化(teaching-learning-based optimization,TLBO)算法是近年来提出的一种通过模拟"教"与"学"行为的群体智能算法。为了克服教与学优化算法容易早熟,解精度较低,后期收敛速度慢等弱点,提出了一种改进的教与学优化算法,并命名为S-TLBO(small world neighborhood TLBO)。该算法采用小世界网络作为其种群的空间结构关系,种群中的个体被看作是网络上的节点。在算法的"教"阶段,学生基于概率向教师个体进行学习,而在"学"阶段,学生则在自己的邻居节点中随机选择较为优秀的个体进行学习。为了提高加强算法的勘探新解和开采能力,引入教师个体执行反向学习算法。在多个经典的测试函数上的实验结果表明,所提出的改进算法具有较高的全局收敛性和解精度,适合于求解较高维度的多模态函数优化问题。
