Much of the world is facing water scarcity during one or the other part of the year. Hence, water resources management and optimal operation of water resources system take on added importance these days. This study in...
详细信息
Much of the world is facing water scarcity during one or the other part of the year. Hence, water resources management and optimal operation of water resources system take on added importance these days. This study introduces an improved version of krill algorithm for reservoir operation. The algorithm is based on adding an onlooker search mechanism to avoid being trapped in local optima and then updating its position. The new krill algorithm is tested using a case study for irrigation management. The computation time is 33 s for the new algorithm but is 54, 59, and 60 s for krill algorithm, particle swarm optimization and genetic algorithm, respectively. Also, the improved krill algorithm can meet 97% of irrigation demands and has the lowest value of vulnerability index among genetic algorithm, particle swarm optimization, and simple krill algorithm. Also, the average solution of improved krill algorithm is close to the global solution. Results indicate that the improved krill algorithm has high potential for application in water resource management.
Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT *** energy consumption of servers and computi...
详细信息
Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT *** energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog *** consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible *** deal with this problem,a binary model based on the combination of the krill Herd algorithm(KHA)and the Artificial Hummingbird algorithm(AHA)is introduced as Binary KHA-AHA(BAHA-KHA).KHA is used to improve ***,the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling(DVFS)*** Heterogeneous Earliest Finish Time(HEFT)method is used to discover the order of task flow *** goal of the BAHA-KHA model is to minimize the number of resources,the communication between dependent tasks,and reduce energy *** this paper,the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog *** results were tested on five different workflows(Montage,CyberShake,LIGO,SIPHT,and Epigenomics).The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA,KHA,PSO and GA *** BAHA-KHA model has reduced the makespan rate by about 18%and the energy consumption by about 24%in comparison with *** is a preview of subscription content,log in via an institution to check access.
In response to the demand for fine-grained management of power consumers by electric grid companies, a composite model is proposed to achieve stratified classification of four types of electricity usage behavior: '...
详细信息
In response to the demand for fine-grained management of power consumers by electric grid companies, a composite model is proposed to achieve stratified classification of four types of electricity usage behavior: 'normal,' 'theft,' 'leakage,' and 'metering anomalies'. Firstly, a three-dimensional feature representation is obtained by dimensionality reduction of high-dimensional electricity usage data using features such as voltage imbalance. Subsequently, a class of support vector machine, KH-OC-SVM (krill-herd optimized one-class support vector machine), optimized by the krill algorithm, is introduced to automatically classify the feature vectors into 'normal' and 'abnormal' categories. Finally, a density-based K-means clustering algorithm is utilized to analyze the 'abnormal' data, automatically categorizing them into three types of abnormal electricity usage behavior: 'theft,' 'leakage,' and 'metering anomalies.' The experimental results demonstrate the effectiveness of the proposed method in achieving automatic classification of power consumers' electricity usage behavior.
To realize the evaluation of English teaching standards and reflect the English teaching standard extend, a series of English teaching quality evaluation indicate approaches based on lecturers' quality, teaching a...
详细信息
Developing optimal operation policy for single or multi-purposes dams and reservoirs is a complex engineering application. The main reasons for such complexity are the stochastic nature of the system input and slow co...
详细信息
Developing optimal operation policy for single or multi-purposes dams and reservoirs is a complex engineering application. The main reasons for such complexity are the stochastic nature of the system input and slow convergence of the optimization method. Furthermore, searching optimal operation for multi purposes or chain reservoir systems, becomes even more complex because of interfering operations between successive dams. In this study, a new hybrid algorithm has been introduced by merging the genetic algorithm (GA) with the krill algorithm. In fact, the proposed hybrid algorithm amalgamates the advantages of both algorithms, first, the ability to converge fast for global optimum and, second, considering the effect of stochastic nature of the system. Three benchmark functions were used to evaluate the performance of this proposed optimization model. In addition, the proposed hybrid algorithm was examined for Karun-4 reservoir in Iran as an example for a hydro-power generation dam. Two benchmark problems of hydropower operations for multi-purposes reservoir systems, namely four-reservoir and ten reservoir systems were considered in the study. Results showed that the proposed hybrid algorithm outperformed the well-developed traditional nonlinear programming solvers, such as Lingo 8 software. (C) 2017 Elsevier Ltd. All rights reserved.
暂无评论