检索结果-内蒙古大学图书馆

Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm

ARTIFICIAL INTELLIGENCE REVIEW 2021年第3期54卷 2221-2257页

作者： Gharehpasha, Sasan Masdari, Mohammad Jafarian, Ahmad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Islamic Azad Univ Dept Math Urmia Branch Orumiyeh Iran

Cloud computing is a computing paradigm, where a large pool of systems is connected in private or public networks to provide dynamically scalable infrastructure for application, data, and file storage. With the advent of this technology, the cost of power computation, application hosting, content storage, resource wastage, and delivery is reduced significantly. Cloud computing provides the possibility of merely concentrating on business goals instead of expanding hardware resources for users. Challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines over physical ones in cloud data centers can lead to the management of resources and prevention of the resources waste. Hereby, a new approach is proposed based on the combination of the hybrid discrete multi-object whale optimization algorithm, multi-verse optimizer with chaotic functions for optimal placement in the cloud data center. The first object of the proposed algorithm is to decrease power consumption, which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut the resource wastage and managing resources using the optimal placement of virtual machines over physical machines in cloud data centers. With this method, the increasing rate of virtual migration to physical machines is prevented. Finally, the results obtained from the proposed algorithm were compared to some algorithms such as first fit, VMPACS, MBFD.

关键词： Cloud computing Virtualization technology whale optimization algorithm Multi-verse optimizer Chaotic functions Power consumption Resource management

来源：评论

学校读者我要写书评

暂无评论

A new metaheuristic approach based on orbit in the multi-objective optimization of wireless sensor networks

引用

WIRELESS NETWORKS 2021年第1期27卷 285-305页

作者： Ozdag, Recep Canayaz, Murat Van Yuzuncu Yil Univ Dept Comp Engn TR-65080 Van Turkey

Wireless sensor networks (WSNs) is a research area which has been used in various applications and has continuously developed up to now. WSNs are used in many applications, especially in military and civilian applications, with the aim of monitoring the environment and tracking objects. For this purpose, increasing the coverage rate of WSNs is one of the important criteria that determine the effective monitoring of the network. Since the sensors that make up the WSNs have a limited capacity in terms of energy, process and memory, various algorithmic solutions have been developed to optimize this criterion. The effective dynamic deployment of sensor nodes, which is the primary goal of these solutions, has a critical role in determining the performance of the network. A new orbit-based dynamic deployment approach based on metaheuristic whale optimization algorithm has been proposed in this study. The goal is to optimize the convergence speed of the nodes, the coverage rate of the network, the total displacement (movement) distances of sensors and the degree ofk-coverage of each target (Grid) point in the area by effectively performing the dynamic deployments of sensors after their random distribution. This approach is compared with MADA-WOA and MADA-EM in the literature. Simulation results indicated that the approach developed in rapidly converging sensors to each other, increasing the network's coverage rate, and in minimizing the total movement distances of the sensors in the area and the degrees ofk-coverage of Grid points covered by the sensors could be proposed.

关键词： Wireless sensor networks Sensor deployment Area coverage problem whale optimization algorithm Binary detection model Degree ofk-coverage

来源：评论

学校读者我要写书评

暂无评论

Optimal Synthesis of Unequally Spaced Linear Arrays under Multiple Constraints

引用

APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2022年第4期39卷 291-296页

作者： Zhao, Zhong-Hui Shandong Univ Sci & Technol Coll Elect & Informat Engn Qingdao 266590 Shandong Peoples R China

This paper proposes a differential evolution modified with adaptive epsilon-constraint handling and whale optimization algorithm (epsilon-WOA-DE) for the synthesis of unequally spaced linear arrays under array layout constraints and array pattern characteristics constraints. In particular, the success history based adaptive differential evolution with linear population reduction (LSHADE) serves as the basic search engine in this study. To ensure the searching ability of LSHADE under multiple constraints, an adaptive epsilon-constraint handling technique is implemented in LSHADE, in which the epsilon level is adjusted dynamically to make the solution scalable to the feasible region when it is in the infeasible region. In addition, the WOA mutation is implemented in the LSHADE to enhance the local search capability. Two array synthesis examples with multiple constraints are chosen to demonstrate the effectiveness of the proposed algorithm. analysis of the epsilon-WOA-DE illustrate the superior capability of the proposed method.

关键词： adaptive epsilon-constraint handling array synthesis differential evolution unequally spaced linear arrays whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A weighted ensemble classifier based on WOA for classification of diabetes

引用

NEURAL COMPUTING & APPLICATIONS 2022年第2期34卷 1613-1621页

作者： Khademi, Fatemeh Rabbani, Mohsen Motameni, Homayun Akbari, Ebrahim Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Islamic Azad Univ Dept Appl Math Sari Branch Sari Iran

Due to the threat and increasing trend to diabetes, different approaches to diagnose it have been proposed, so that classification is one of the main techniques. In this article ultimate aim is designing a novel system to diagnose diabetes. To this end, we use an ensemble classifier to apply support vector machine (SVM), k-nearest neighbor (KNN), and whale optimization algorithm (WOA). WOA is responsible for generating weights for each classifier to improve the accuracy of the diabetes classification. For our empirical study, we gathered a dataset of diabetes from medical centers in Iran. The implementation results showed that the designed ensemble classifier achieved the accuracy rate of 83%, which means it improved the accuracy of the best preceding classifier about 5%. Moreover, the designed ensemble classifier based on WOA improved the accuracy about 1% in comparison with PSO that is preceding the WOA in terms of accuracy level.

关键词： Diabetes Classification Support vector machine K-nearest neighbor whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT

引用

THEORETICAL COMPUTER SCIENCE 2022年 927卷 87-97页

作者： Khan, Mohd Abdul Rahim Shavkatovich, Shavkatov Navruzbek Nagpal, Bharti Kumar, Anil Haq, Mohd Anul Tharini, V. Jeevika Karupusamy, Sathishkumar Alazzam, Malik Bader Majmaah Univ Coll Comp & Informat Sci Dept Comp Sci Al Majmaah 11952 Saudi Arabia Tashkent Inst Finance Dept Corp Finance & Secur Tashkent Uzbekistan AIACT&R NSUT East Campus Delhi India DIT Univ Sch Comp Data Sci Res Grp Dehra Dun India Majmaah Univ Coll Comp & Informat Sci Dept Comp Sci Al Majmaah 11952 Saudi Arabia Sri Ramakrishna Coll Arts & Sci Dept Comp Sci Coimbatore Tamilnadu India Gobi Arts & Sci Coll Autonomous Dept Comp Sci Gobichettipalayam Tamilnadu India Ajloun Natl Univ Informat Technol Dept Ajloun Jordan

The Internet of Things (IoT) has subsequently been applied to a variety of sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the Internet of Things has enormous promise in a wide range of applications, there still are certain areas where it may be improved. Designers had focused our present research on reducing the energy consumption of devices in IoT networks, which will result in a longer network lifetime. The far more suitable Cluster Head (CH) throughout the IoT system is determined in this study to optimize energy consumption. whale optimization algorithm (WOA) with Evolutionary algorithm (EA) is indeed a mixed meta-heuristic algorithm used during the suggested study. Various quantifiable metrics, including the variety of adult nodes, workload, temperatures, remaining energy, and a target value, were utilized IoT network groups. The suggested method then is contrasted to several cutting-edge optimization techniques, including the Artificial Bee Colony method, Neural Network, Adapted Gravity Simulated annealing. The findings show that the suggested hybrid method outperforms conventional methods. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Cluster head IoT Meta-heuristic method whale optimization algorithm Wireless network

来源：评论

学校读者我要写书评

暂无评论

A new fusion of whale optimizer algorithm with Kapur's entropy for multi-threshold image segmentation: analysis and validations

引用

ARTIFICIAL INTELLIGENCE REVIEW 2022年第8期55卷 6389-6459页

作者： Abdel-Basset, Mohamed Mohamed, Reda Abouhawwash, Mohamed Zagazig Univ Zagazig 2 Zagazig 44519 Ash Sharqia Gov Egypt Mansoura Univ Dept Math Fac Sci Mansoura 35516 Egypt Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

The separation of an object from other objects or the background by selecting the optimal threshold values remains a challenge in the field of image segmentation. Threshold segmentation is one of the most popular image segmentation techniques. The traditional methods for finding the optimum threshold are computationally expensive, tedious, and may be inaccurate. Hence, this paper proposes an Improved whale optimization algorithm (IWOA) based on Kapur's entropy for solving multi-threshold segmentation of the gray level image. Also, IWOA supports its performance using linearly convergence increasing and local minima avoidance technique (LCMA), and ranking-based updating method (RUM). LCMA technique accelerates the convergence speed of the solutions toward the optimal solution and tries to avoid the local minima problem that may fall within the optimization process. To do that, it updates randomly the positions of the worst solutions to be near to the best solution and at the same time randomly within the search space according to a certain probability to avoid stuck into local minima. Because of the randomization process used in LCMA for updating the solutions toward the best solutions, a huge number of the solutions around the best are skipped. Therefore, the RUM is used to replace the unbeneficial solution with a novel updating scheme to cover this problem. We compare IWOA with another seven algorithms using a set of well-known test images. We use several performance measures, such as fitness values, Peak Signal to Noise Ratio, Structured Similarity Index Metric, Standard Deviation, and CPU time.

关键词： Image segmentation whale optimization algorithm Linearly convergence Local Minima Kapur's entropy

来源：评论

学校读者我要写书评

暂无评论

A novel aspect-based sentiment classifier using whale optimized adaptive neural network

引用

NEURAL COMPUTING & APPLICATIONS 2022年第5期34卷 4003-4012页

作者： Balaganesh, N. Muneeswaran, K. Mepco Schlenk Engn Coll Autonomous Dept Comp Sci & Engn Sivakasi Tamil Nadu India

In the field of e-commerce applications, nowadays the aspect-based sentiment analysis has become vital and every consumer started focusing on various aspects of the product before making the purchase decision through online portals like Amazon, Walmart, Alibaba, Flipkart, etc. Hence, the enhancement of sentiment classification considering every aspect of product and services is in the limelight. In this proposed research, aspect-based sentiment classification model has been developed employing sentiment whale optimized adaptive neural network (SWOANN) for classifying the sentiment of key aspects of products and services. The proposed work uses the key features such as the positive opinion score, negative opinion score and the term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. Moreover, the computational speed and accuracy of sentiment classification of the product and services have been improved by the optimal selection of weights of the neurons of proposed model. The promising results are obtained by analysing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed model can be compatible with any sentiment classification problem of products and services.

关键词： Aspect-based sentiment analysis whale optimization algorithm Artificial neural network Opinion mining Term frequency-inverse document frequency

来源：评论

学校读者我要写书评

暂无评论

whale optimization-based Synthetic Minority Oversampling Technique for Binary Imbalanced Datasets

引用

Procedia Computer Science 2024年 235卷 250-263页

作者： Pooja Tyagi Jaspreeti Singh Anjana Gosain USIC&T Guru Gobind Singh Indraprastha University Sector 16 Dwarka New Delhi India

The problem of class imbalance has become a predominant area of research recently. Synthetic Minority Oversampling Technique (SMOTE) stands as a popular and widely adopted oversampling technique that effectively addresses the challenge of class imbalance. However, its performance relies on one of the critical parameters i.e., the number of nearest neighbors, k_neighbors , which is often arbitrarily chosen by the users due to which it may not yield optimal results. Furthermore, the varying imbalance ratios across datasets add complexity to the task of parameter selection in SMOTE. In an effort to address this issue, this paper proposes a hybrid rebalancing technique called whale optimization algorithm based SMOTE (WOA-SMOTE) that combines a metaheuristic technique, WOA, with SMOTE. The algorithm utilizes the advantages of WOA in finding the optimal value of k_neighbors of SMOTE which is crucial in generating synthetic samples that represent the distribution of samples more appropriately, thereby improving the performance of classifiers on imbalanced datasets. The study evaluates the performance of WOA-SMOTE alongside 6 benchmark sampling techniques on 10 real-world imbalanced datasets from Keel repository. These belong to different domains with imbalance ratio (IR) ranging from 1.25 to 15.46. Four different classifiers are used and the evaluation is based on three performance measures: AUC, g-mean and F1 scores. The experimental results showcase WOA-SMOTE's superior performance over SMOTE in majority of the datasets. Notably, WOA-SMOTE outperforms existing techniques in terms of F1 scores when using SVM and XGBoost classifiers in 8 out of 10 datasets. Moreover, its performance remains impressive in 6 of these datasets with random forest and logistic regression classifiers.

关键词： class imbalance problem oversampling under sampling SMOTE metaheuristic algorithms whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive Active Noise Cancellation Method of High Voltage Reactor Based on DnCNN Network 6

Adaptive Active Noise Cancellation Method of High Voltage Re...

引用

6th International Conference on Machine Learning and Intelligent Systems, MLIS 2024

作者： Han, Li Lu, Bin Fang, Jizhong Zhang, Qingxia Qian, Yong Ma, Rui Guo, Wei Cheng, Cheng Electric Power Research Institute of State Grid Ningxia Electric Power Company China Shanghai Shenjie Environment Protection Science and Technology Co Ltd China Ultra-high Voltage Company of State Grid Ningxia Electric Power Company China

ISBN: (纸本)9781643685533

Current noise cancellation methods can barely deal with nonlinear signals in an effective way and the overall noise reduction performance is relatively poor. Aiming at these issues, we propose an adaptive active noise cancellation (ANC) method of high voltage reactors based on the denoising convolutional neural network (DnCNN). First, a feedforward adaptive ANC system was designed by considering the noise characteristics of high voltage reactors. Then, a noise control model was constructed and the weight coefficient of the filter was optimized by using the DnCNN network;meanwhile, the inverse waveform was reconstructed by adding spectrum analysis, thereby achieving effective control of noise signals. Additionally, the whale optimization algorithm was employed to solve the problem models of minimization of total radiation power of different sound sources, and the optimized location, amplitude, and phase angle of the secondary sound sources were determined. Based on LabVIEW, an online noise monitoring and ANC system was developed and used for experimental analysis of the proposed method. The results showed that the noise reduction performance was optimized (the overall noise reduction = 9 dB) when the number of secondary sound sources was 8 and the distance from the primary sound source was 1.0 m, suggesting that the proposed method can reliably reduce the noises of high voltage reactors. © 2024 The Authors.

关键词： adaptive active noise cancellation system DnCNN network high voltage reactor parameter optimization of secondary sound sources spectrum analysis whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Wind Power Correction Prediction Considering Similar Wind Power Climbing Events

Wind Power Correction Prediction Considering Similar Wind Po...

引用

International Conference on Energy Engineering, ICEE 2023

作者： Li, Kai Shi, Kaiming Ma, Ruiming Sang, Shengpeng Cao, Shitong Gou, Yuliang College of Electric Power Inner Mongolia University of Technology Inner Mongolia Hohhot010051 China Electric Engineering Technology Research Institute of Inner Mongolia Energy Power Investment Group Co. Ltd. Inner Mongolia Hohhot010090 China

ISBN: (纸本)9789819771455

In response to the issues of wind turbine ramp events affecting the safe and stable operation of power systems and the accuracy of wind power prediction, a wind power correction prediction method is proposed considering similar ramp events. The whale optimization algorithm (WOA) is utilized to optimize the parameters of the Least Squares Support Vector Machine (LSSVM) for initial wind power prediction. Extracting ramp events by using the wind power ramp identification method based on extremum point extraction from the predicted results. To cope with the variable time windows of the identified ramp events, the Dynamic Time Warping (DTW) method is employed to identify similar ramp events from historical data. Finally, a second prediction is conducted based on similar historical ramp events to obtain the corrected wind power prediction. Experimental results show that the proposed method can improve the short-term prediction accuracy of wind power effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： dynamic time warping least squares support vector machine whale optimization algorithm wind power prediction wind power ramp events

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：