Task scheduling on heterogeneous data centers of cloud computing environment is a challenging problem. The efficiency of the cloud depends on the adopted task scheduling strategy. The task scheduling algorithm schedul...
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Task scheduling on heterogeneous data centers of cloud computing environment is a challenging problem. The efficiency of the cloud depends on the adopted task scheduling strategy. The task scheduling algorithm schedules the required task resources of application in the cloud platform. Even though many algorithms are presented for task scheduling;they consider only the minimum objectives for the trade-off of optimal scheduling. In this paper, a multi-objective task scheduling strategy is proposed for the task scheduling problem in the cloud network as an NP-hard optimization problem. In order to solve the scheduling problem, Fractional greywolf Multi-objective optimization-based Task Scheduling strategy (FGMTS) is newly proposed for scheduling tasks in the cloud. The proposed FGMTS algorithm is the combination of the existing fractional theory and greywolf Optimizer algorithm. Also, the multi-objective function is newly formulated to solve the multi-objective scheduling problem. The fitness function for the proposed optimization considers the parameters, such as Execution time, Communication time, Execution cost, Communication cost, Energy, and Resource utilization for optimal scheduling. The experimentation of the proposed task scheduling strategy is carried out over two cloud setups. The performance of proposed system is validated over the existing techniques, such as PSO, GA, and GWO using the metrics considered in the multi-objective formulation function. The experimental results show that the proposed FGMTS-Task scheduling scheme allocates the resource for all incoming task requests while preserving the performance of the cloud with an increase in the profit.
While the economic is rapidly developing, human beings are facing a serious ecological problems, sustainable-development has got attention from more and more countries and has been treated as an economic and social de...
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While the economic is rapidly developing, human beings are facing a serious ecological problems, sustainable-development has got attention from more and more countries and has been treated as an economic and social development strategy. The development and utilization of renewable energy can realize the transformation of regional economic development mode and promote the long-term sustainable development of regional economy. One of the latest concepts that has attracted a lot of attention in the power systems is the energy hub (EH). In this paper, a combined energy system (CES) known as the EH consisting of electrical, cooling and heating equipment along with the demand response programs (DRPs) as well as renewable energy resources (RERs) optimization study have been proposed. Moreover, the uncertainty modeling and electrical scenario creation of cooling, heating, wind speed, solar irradiation and the energy carriers prices including electricity and natural gas is presented. The objective of the optimization problem is maximizing the profit of the EH with the existence of DRPs and RERs under four scenarios which is solved by enhanced greywolfoptimization (GWO) algorithm. In order to avoid such deficiencies and to realize a stabilized relationship between exploration and exploitation, a new modified greywolfoptimization (MGWO) algorithm is proposed. The implementation results show high accuracy and power levels of this method to solve the aforementioned problem under different uncertain parameters and various scenarios. The simulation results of proposed EH profit maximization model shows a reduction in purchased electricity from the main grid and a reduction of the overall operation costs.
greywolfoptimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of greywolfs present in nature. In this p...
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ISBN:
(纸本)9781509032945
greywolfoptimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of greywolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.
This research applies the grey wolf optimization algorithm which includes the feature selection stage and implements borda count method to optimize the function selection problem in classification. The greywolf optim...
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ISBN:
(纸本)9781538666630
This research applies the grey wolf optimization algorithm which includes the feature selection stage and implements borda count method to optimize the function selection problem in classification. The greywolfoptimization mimics the characteristics and movement of wolves which have more than one leader in a pack. The proposed algorithm presents the pack can have more than one pack which selects the most relevant features. The performance of proposed algorithm is compared with other classification techniques such as cAnt-Miner, C4.5 and PART. The experimental results show that the proposed algorithm is capable to optimized feature selection in classification problems.
greywolf Optimizer is a kind of artificial intelligence optimizationalgorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initia...
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greywolf Optimizer is a kind of artificial intelligence optimizationalgorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initial population to increase population diversity;and uses simulated annealing algorithm's strong ability to jump out of local optimum solution to make up for the shortcoming that GWO is easy to fall into local optimum;finally, the first three individuals of population fitness are mutated to improve the improvement of the algorithm. The speed and accuracy of the algorithm are improved to avoid falling into local optimum. The superiority of the improved algorithm is verified by simulation experiments.
A new optimizationalgorithm approach is proposed named grey wolf optimization algorithm for the solution of reactive power planning (RPP) of a connected power network based on the detection of weak buses determined b...
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ISBN:
(纸本)9781509001286
A new optimizationalgorithm approach is proposed named grey wolf optimization algorithm for the solution of reactive power planning (RPP) of a connected power network based on the detection of weak buses determined by line stability index method and Fast voltage stability index. The optimizationalgorithm is then applied on standard IEEE 30 and IEEE 57 bus system. The objective of the proposed work is to minimize active power loss and operating cost by proper coordination of reactive power generation of the generators, transformer tap setting positions and shunt capacitors placed at the weak buses of the system while maintaing voltage profile within acceptable limit. Finally, it is observed that reactive power planning with line stability index method yields better result than planning with fast voltage stability index.
In this paper, the problem of optimal power flow (OPF) is solved in a system integrated with wind farms with the aim of reducing the cost of power production and the reduction of power grid loss by applying the grey W...
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ISBN:
(纸本)9781509064069
In this paper, the problem of optimal power flow (OPF) is solved in a system integrated with wind farms with the aim of reducing the cost of power production and the reduction of power grid loss by applying the greywolfoptimization (GWO) algorithm. The variable nature of wind farm output is modeled using two additional cost components corresponding to the states of under estimation and over estimation, where the available power is higher and lower than the scheduled output, respectively. On one side, in the case where there is lower power regards to the planned power, a penalty is added to the cost function. On the other side, if the produced power would be more than the planned power, an additional cost would be added to the cost function because of not buying the overall power of the wind farms. A recently introduced optimization method known as grey wolf optimization algorithm is employed in this article. The problem of OPF based on proposed approach has been applied on a modified version of IEEE 30-bus test system. The results of this study are compared with the results of Genetic algorithm (GA). The results show the superiority of the proposed method, both in the convergence speed as well as the final result comparing to other method.
This paper deals with automatic generation control of multi area power system using a Fuzzy PID controller. The controller parameters are optimized by greywolf Optimizer (GWO) algorithm. Initially, Hydro-Thermal-Gas ...
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This paper deals with automatic generation control of multi area power system using a Fuzzy PID controller. The controller parameters are optimized by greywolf Optimizer (GWO) algorithm. Initially, Hydro-Thermal-Gas two area power systems is considered and superiority of the proposed controller is verified by comparing the results with GWO optimized classical PID controller as well as recently published optimal controller, such as DE-PID and TLBO-PID controllers. The proposed methodology is also verified with a modified power system with a nuclear plant and HVDC link and reveals better performance when compared with sliding mode controller tuned by TLBO algorithm. The proposed controller is designed to stabilize the frequency deviations of nonlinear power system considering FACTS devices and SMES. The results reveal that IPFC seems to be a promising alternative for frequency and tie-line power stabilization. Also the proposed controller is robust and satisfactory towards random step and sinusoidal load patterns.
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