The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource pr...The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.展开更多
Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengt...Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.展开更多
为了提升变电站数据流检测的实时性与准确性,提出一种使用白鲸优化(beluga whale optimization,BWO)算法优化基于密度的噪声应用空间聚类(density based spatial clustering of applications with noise,DBSCAN)算法,与使用圆圈搜索算法...为了提升变电站数据流检测的实时性与准确性,提出一种使用白鲸优化(beluga whale optimization,BWO)算法优化基于密度的噪声应用空间聚类(density based spatial clustering of applications with noise,DBSCAN)算法,与使用圆圈搜索算法(circle search algorithm,CSA)优化单分类正则核极限学习机(one class regularized kernel extreme learning machine,OCRKELM)相结合的变电站通信网络数据流异常检测方法。首先,利用BWO-DBSCAN对正常数据流进行聚类,形成样本簇;其次,通过CSA-OCRKELM模型对异常数据流进行实时检测;最后,利用OPNET仿真软件仿真模拟变电站的通信行为并进行对比分析,验证所提方法的有效性。仿真实验结果表明所构建检测模型的检测率约为99%,较其他检测模型具有较高的性能与准确率。展开更多
随着射频识别(Radio Frequency Identification,RFID)技术的发展,人们对其应用的要求越来越高,在阅读器部署方面的研究也逐渐深入。为了解决规定区域内RFID阅读器位置规划问题,在划定的区域内,以标签覆盖率、阅读器间的碰撞干扰、负载...随着射频识别(Radio Frequency Identification,RFID)技术的发展,人们对其应用的要求越来越高,在阅读器部署方面的研究也逐渐深入。为了解决规定区域内RFID阅读器位置规划问题,在划定的区域内,以标签覆盖率、阅读器间的碰撞干扰、负载均衡为目标来建立数学优化模型,在白鲸算法的基础上提出了一种改进型白鲸算法。首先,针对标准白鲸算法存在易陷入局部最优、丢失次优解的缺陷,提出了一种更新精英群体机制;其次,为了增强算法的探索能力,加入了反向学习策略;最后,运用该算法来解决RFID网络规划问题。通过在一定环境中放置不同数量集群和随机分布的标签,将改进型白鲸算法与粒子群算法、灰狼算法和标准白鲸算法进行对比。仿真结果表明,在相同环境下,改进型白鲸算法的性能相比粒子群算法平均提高了21.1%,比灰狼算法提高了28.5%,比白鲸算法提高了3.3%,说明该算法相比其他3种算法在搜索精度上具有更好的性能,并通过阅读器优化部署测试,验证了该应用的有效性和可行性。展开更多
文摘The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2024R809).
文摘Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.
文摘为了提升变电站数据流检测的实时性与准确性,提出一种使用白鲸优化(beluga whale optimization,BWO)算法优化基于密度的噪声应用空间聚类(density based spatial clustering of applications with noise,DBSCAN)算法,与使用圆圈搜索算法(circle search algorithm,CSA)优化单分类正则核极限学习机(one class regularized kernel extreme learning machine,OCRKELM)相结合的变电站通信网络数据流异常检测方法。首先,利用BWO-DBSCAN对正常数据流进行聚类,形成样本簇;其次,通过CSA-OCRKELM模型对异常数据流进行实时检测;最后,利用OPNET仿真软件仿真模拟变电站的通信行为并进行对比分析,验证所提方法的有效性。仿真实验结果表明所构建检测模型的检测率约为99%,较其他检测模型具有较高的性能与准确率。
文摘随着射频识别(Radio Frequency Identification,RFID)技术的发展,人们对其应用的要求越来越高,在阅读器部署方面的研究也逐渐深入。为了解决规定区域内RFID阅读器位置规划问题,在划定的区域内,以标签覆盖率、阅读器间的碰撞干扰、负载均衡为目标来建立数学优化模型,在白鲸算法的基础上提出了一种改进型白鲸算法。首先,针对标准白鲸算法存在易陷入局部最优、丢失次优解的缺陷,提出了一种更新精英群体机制;其次,为了增强算法的探索能力,加入了反向学习策略;最后,运用该算法来解决RFID网络规划问题。通过在一定环境中放置不同数量集群和随机分布的标签,将改进型白鲸算法与粒子群算法、灰狼算法和标准白鲸算法进行对比。仿真结果表明,在相同环境下,改进型白鲸算法的性能相比粒子群算法平均提高了21.1%,比灰狼算法提高了28.5%,比白鲸算法提高了3.3%,说明该算法相比其他3种算法在搜索精度上具有更好的性能,并通过阅读器优化部署测试,验证了该应用的有效性和可行性。
