In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of clou...
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In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid coot-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid coot-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques.
Mobile Crowd Sensing (MCS) has become a new archetype allowing individuals to effectively perform their sensing tasks by employing interested mobile users. This paradigm satisfies the requirements of the task requeste...
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Mobile Crowd Sensing (MCS) has become a new archetype allowing individuals to effectively perform their sensing tasks by employing interested mobile users. This paradigm satisfies the requirements of the task requester, along with providing the willing participants with a way to generate profit by performing the specific tasks. Normally, an incentive is provided to the participants by the requester for processing the requested tasks. However, the requester may normally have a limited budget, so they prefer to make payments to the user providing good quality data instead of all the users participating in the process. Thus, selecting the most suitable user among the participant pool is required for executing the tasks efficiently in a short time. This paper presents an efficient online task allocation technique using a hybrid optimization approach. A novel Crow coot Foraging Optimization (CCFO) algorithm is proposed for allocating tasks in MCS. The optimal user is chosen based on the fitness function devised using various aspects, like finish time, time of receiving task, time of sending task, makespan, monetary cost, ready time, and energy consumption. The CCFO algorithm is developed by modifying the C-BFO algorithm to the coot algorithm to enhance the performance of the task allocation process. The presented CCFO technique for task allocation based on the fitness function evaluates makespan with the lowest value of 0.482.
作者：
Wang, ZongyaoPeng, QiyangRao, WeiLi, DanNanchang Inst Technol
Sch Informat Engn Nanchang 330099 Peoples R China Fudan Univ
Sch Informat Sci & Technol Key Lab Informat Sci Electromagnet Waves Shanghai 200433 Peoples R China Fudan Univ
Res Ctr Smart Networks & Syst Sch Informat Sci & Technol Shanghai 200433 Peoples R China
Addressing the shortcomings of the Sparrow Search algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search algorithm (MISSA) is propos...
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Addressing the shortcomings of the Sparrow Search algorithm (SSA), such as low accuracy of convergence and tendency of falling into local optimum, a Multi-strategy Integrated Sparrow Search algorithm (MISSA) is proposed. In this method, by improving the black-winged kite algorithm and applying it to the producer's position update formula, an improved search strategy (ISS) is firstly proposed to enhance search ability. Secondly, a new strategy inspired by the coot algorithm, called the group follow strategy (GFS), is proposed to improve the ability to jump out of the local optimum. Finally, a proposed random opposition-based learning strategy (ROBLS) is applied to the population after each iteration to enhance its diversity. To verify MISSA's effectiveness, extensive testing is conducted on 24 benchmark functions as well as CEC 2017 functions. The experimental results, complemented by Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA and other advanced optimization algorithms, exhibiting superior overall performance.
With the recent advancements in technology, there has been a tremendous growth in the usage of images captured using satellites in various applications, like defense, academics, resource exploration, land-use mapping,...
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With the recent advancements in technology, there has been a tremendous growth in the usage of images captured using satellites in various applications, like defense, academics, resource exploration, land-use mapping, and so on. Certain mission-critical applications need images of higher visual quality, but the images captured by the sensors normally suffer from a tradeoff between high spectral and spatial resolutions. Hence, for obtaining images with high visual quality, it is necessary to combine the low resolution multispectral (MS) image with the high resolution panchromatic (PAN) image, and this is accomplished by means of pansharpening. In this paper, an efficient pansharpening technique is devised by using a hybrid optimized deep learning network. Zeiler and Fergus network (ZF Net) is utilized for performing the fusion of the sharpened and upsampled MS image with the PAN image. A novel Dingo coot (DICO) optimization is created for updating the learning parameters and weights of the ZF Net. Moreover, the devised DICO_ZF Net for pansharpening is examined for its effectiveness by considering measures, like Peak Signal To Noise Ratio (PSNR) and Degree of Distortion (DD) and is found to have attained values at 50.177 dB and 0.063 dB.
This research article emphasizes the combined automatic generation control (AGC) and automatic voltage regulator (AVR) problem for an interconnected hybrid system having electric vehicles, renewable energy sources (RE...
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This research article emphasizes the combined automatic generation control (AGC) and automatic voltage regulator (AVR) problem for an interconnected hybrid system having electric vehicles, renewable energy sources (RES) like wind, solar photovoltaic, solar-thermal in addition to conventional thermal units. A novel cascade fractional order (FO) controller named FO PID-FO PD (cascade PIAD mu-PD mu) is proposed to suppress the oscillation in AGC and AVR system whose parameters are obtained using recent optimization technique called coot algorithm (CA). Findings show that proposed cascade PIAD mu-PD mu controller outperformed PID and FO PID controllers in providing better dynamics. The CA provides improved performance compared to popularly known particle swarm optimization and firefly algorithm not only in dynamics but also in computational burden. The inclusion of practical nonlinearities made the system more oscillatory. The asynchronous HVDC link helps in improving the performance. The impact FACTS devices such as interline power flow controller and gate-controlled series capacitor (GCSC) are studied in the system and can suppress the oscillatory dynamics. For the first time, the combined GCSC and HVDC link effect is examined and improved dynamic performance. The ultracapacitor storage device showed its efficiency for regulating oscillations in the presence of RES. The proposed cascade PIAD mu-PD mu performance is evaluated for randomized load demand, wind power and solar irradiations and is seen as effective over the FO PID controller. Sensitivity analysis reveals the cascade PIAD mu-PD mu parameters are robust. Moreover, the study is also performed on OPAL-RT OP 4510 for real-time validation.
The combined heat and power economic dispatch (CHPED) problem is a non-convex multivariate global optimization problem. The objective of the problem is to reduce total production costs while imposing a variety of cons...
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The combined heat and power economic dispatch (CHPED) problem is a non-convex multivariate global optimization problem. The objective of the problem is to reduce total production costs while imposing a variety of constraints and meeting the demand for power and heat. Three recently presented metaheuristic approaches, Slime Mould algorithm (SMA), coot algorithm and Marine Predators algorithm (MPA), are applied for solving CHPED problem. Studies dealing with the CHPED problem in the literature often do not consider valve points effect, prohibited operation zones for power-only units, feasible region constraints of combined heat and power units, all at once. Furthermore, power losses are neglected especially in large-scale problems. In this study, the CHPED problem is solved by considering all operational constraints including active power transmission losses. Three separate case studies with dimensions of 11 units, 48 units, and 96 units were used in the tests under various limitations. The experimental results revealed that MPA outperformed not only SMA, and coot but also the algorithms proposed previously in the literature.
Solar radiation and wind speed are the fundamental parameters for the design and operations of solar and wind energy systems. Renewable energy sources (RESs) are intermittent and dependent on different atmospheric par...
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ISBN:
(纸本)9781665495523
Solar radiation and wind speed are the fundamental parameters for the design and operations of solar and wind energy systems. Renewable energy sources (RESs) are intermittent and dependent on different atmospheric parameters. Therefore, it is crucial to accurately forecast RESs, such as solar radiation and wind speed. In this study, a deep learning-based random forest technique is proposed to predict solar radiation and wind speed. A novel coot algorithm (CA) is suggested to optimize the number of decision trees of the random forest model, and the performance of the CA is compared with the existing particle swarm optimization (PSO) technique. The results show that the performance of CA is better than PSO.
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