检索结果-内蒙古大学图书馆

Improved whale optimization variants for SLA-compliant placement of virtual machines in cloud data centers

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 149-171页

作者： Mehta, Shikha Kaur, Parmeet Agarwal, Parul Jaypee Inst Informat Technol Noida India

Infrastructure as a Service (IaaS) model of cloud computing provides resources, such as storage and computation power from cloud-based physical machines to users in the form of virtual machines (VM). This can be cost-efficient for users as well as cloud service providers if the physical machines and related resources are optimally utilized. At the same time, it is also imperative that energy consumption by physical machines in cloud data centers be minimized. A solution to both these challenges can be found by efficient mapping of VMs onto physical machines. This is known as the VM placement problem and the paper aims to find an optimal mapping of VMs to PMs with two objectives. Firstly, the cloud service provider's cost is reduced by increasing the resource utilization and secondly, the energy consumption is reduced by decreasing the number of PMs in use at a time. In this work, two variants of whale optimization algorithm (WOA) are proposed for efficiently placing VMs on the physical machines. In the first variant exploitation phase of WOA is improved via simulated annealing (SWOA). In the second variant Levy (L) distribution is incorporated to improve the exploration phase of WOA (LWOA). Performance of algorithms is assessed on varying workloads under deadline constraints. Experiments are performed on light, medium, heavy and very heavy workloads. Results demonstrate that the proposed variant SWOA provide efficient solutions on light workload while LWOA gives efficient solution on other workloads for the VM placement problem. These algorithms also converge faster as compared to its contemporary counterparts thereby reducing the computational complexity of the problem.

关键词： Cloud computing Virtual machine placement whale optimization algorithm SLA PSO

来源：评论

学校读者我要写书评

暂无评论

Swarm-based hybrid optimization algorithms: an exhaustive analysis and its applications to electricity load and price forecasting

引用

SOFT COMPUTING 2023年第19期27卷 14095-14126页

作者： Kottath, Rahul Singh, Priyanka Bhowmick, Anirban Bentley Syst India Pvt Ltd Digital Tower Pune India SRM Univ AP Dept Comp Sci & Engn Amaravati 522502 Andhra Pradesh India VIT Bhopal Univ Sch Elect & Elect Engn Bhopal 466114 Madhya Pradesh India

In this work, we intend to propose multiple hybrid algorithms with the idea of giving a choice to the particles of a swarm to update their position for the next generation. To implement this concept, Cuckoo Search algorithm (CSA), Grey Wolf optimization (GWO), Harris Hawks optimization (HHO), and whale optimization algorithm (WOA) have been utilized. Exhaustive possible combinations of these algorithms are developed and benchmarked against the base algorithms. These hybrid algorithms have been validated on twenty-four well-known unimodal and multimodal benchmarks functions, and detailed analysis with varying dimensions and population size is discussed for the same. Further, the efficacy of these algorithms has been tested on short-term electricity load and price forecasting applications. For this purpose, the algorithms have been combined with Artificial Neural Networks (ANNs) to evaluate their performance on the ISO New Pool England dataset. The results demonstrate that hybrid optimization algorithms perform superior to their base algorithms in most test cases. Furthermore, the results show that the performance of CSA-GWO is significantly better than other algorithms.

关键词： Artificial neural network Cuckoo search algorithm Grey wolf optimization Harris hawks optimization whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved whale optimization with Local-Search Method for Feature Selection

引用

Computers, Materials & Continua 2023年第4期75卷 1371-1389页

作者： Malek Alzaqebah Mutasem KAlsmadi Sana Jawarneh Jehad Saad Alqurni Mohammed Tayfour Ibrahim Almarashdeh Rami Mustafa A.Mohammad Fahad A.Alghamdi Nahier Aldhafferi Abdullah Alqahtani Khalid A.Alissa Bashar A.Aldeeb Usama A.Badawi Maram Alwohaibi Hayat Alfagham Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Basic&Applied Scientific Research Center Imam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Community CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Educational Technology College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Various feature selection algorithms are usually employed to improve classification models’overall *** algorithms typically accompany such algorithms to select the optimal set of *** the most currently attractive trends within optimization algorithms are hybrid *** present paper presents two Stages of Local Search models for feature selection based on WOA(whale optimization algorithm)and Great Deluge(GD).GD algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the *** version is employed using the best solution found by the WOA algorithm and exploited by the GD *** addition,disruptive selection(DS)is employed to select the solutions from the population for local *** is chosen to maintain the diversity of the population via enhancing low and high-quality ***(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’***,a comparison was made with four population-based algorithms as wrapper feature selection methods from the *** proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper ***,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

关键词： optimization whale optimization algorithm great deluge algorithm feature selection and classification

来源：评论

学校读者我要写书评

暂无评论

optimization of solid oxide fuel cell power generation voltage prediction based on improved neural network

引用

INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2023年第18期18卷 464-472页

作者： Wei, Liming Wang, Yixuan Jilin Jianzhu Univ Sch Elect & Comp Engn Changchun Jilin Peoples R China

This paper proposes a method for predicting the generation voltage of a solid oxide fuel cell based on the data results of a stand-alone solid oxide fuel single cell simulation model under ideal conditions, with the aim of improving the generation efficiency and extending the service life of the solid oxide fuel cell. In this paper, a modified back propagation (BP) neural network algorithm is used to improve the prediction accuracy of the solid oxide fuel cell generation voltage by using the whale algorithm to optimize the BP neural network model to improve its convergence and achieve the effect of improving the prediction accuracy. First, the characteristics of the independent solid oxide fuel cell are introduced and simulated. Second, the long short-term memory network model, linear regression network model and BP neural network are simulated and compared, and the results show that the BP neural network prediction model is more accurate and can be optimized and improved. Finally, the BP neural network is optimized and simulated using the whale algorithm, and the simulation results show that the method has better convergence and higher prediction accuracy than the traditional BP neural network prediction model.

关键词： whale optimization algorithm BP neural network algorithm generation voltage prediction optimization solid oxide fuel cell

来源：评论

学校读者我要写书评

暂无评论

Combining deep reinforcement learning with heuristics to solve the traveling salesman problem

引用

Chinese Physics B 2025年第1期34卷 96-106页

作者： Li Hong Yu Liu Mengqiao Xu Wenhui Deng School of Software Technology Dalian University of TechnologyDalian 116620China School of Economics and Management Dalian University of TechnologyDalian 116024China

Recent studies employing deep learning to solve the traveling salesman problem(TSP)have mainly focused on learning construction *** methods can improve TSP solutions,but still depend on additional ***,methods that focus on learning improvement heuristics to iteratively refine solutions remain *** improvement heuristics are guided by a manually designed search strategy and may only achieve limited *** paper proposes a novel framework for learning improvement heuristics,which automatically discovers better improvement policies for heuristics to iteratively solve the *** framework first designs a new architecture based on a transformer model to make the policy network parameterized,which introduces an action-dropout layer to prevent action selection from *** then proposes a deep reinforcement learning approach integrating a simulated annealing mechanism(named RL-SA)to learn the pairwise selected policy,aiming to improve the 2-opt algorithm's *** RL-SA leverages the whale optimization algorithm to generate initial solutions for better sampling efficiency and uses the Gaussian perturbation strategy to tackle the sparse reward problem of reinforcement *** experiment results show that the proposed approach is significantly superior to the state-of-the-art learning-based methods,and further reduces the gap between learning-based methods and highly optimized solvers in the benchmark ***,our pre-trained model M can be applied to guide the SA algorithm(named M-SA(ours)),which performs better than existing deep models in small-,medium-,and large-scale TSPLIB ***,the M-SA(ours)achieves excellent generalization performance in a real-world dataset on global liner shipping routes,with the optimization percentages in distance reduction ranging from3.52%to 17.99%.

关键词： traveling salesman problem deep reinforcement learning simulated annealing algorithm transformer model whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An intelligent optimization technique for performance improvement in radial distribution network

引用

INTERNATIONAL JOURNAL OF INTELLIGENT UNMANNED SYSTEMS 2025年第1期13卷 38-53页

作者： Vempalle, Rafi Kumar, Dhal Pradyumna Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Elect & Elect Engn Chennai India

PurposeThe demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss ***/methodology/approachIn this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization *** proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid ***/valueThe main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.

关键词： Genetic algorithm Particle swarm optimization Radial distribution system Crow search Multi-verse optimization whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

SURAKSIT: A Secure Resource Allocation Scheme Using whale optimization for D2D Communication 10

SURAKSIT: A Secure Resource Allocation Scheme Using Whale Op...

引用

10th IEEE International Conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Jadav, Nilesh Kumar Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India

ISBN: (纸本)9798350385939;9798350385922

As the demand for mobile data traffic continues to rise, short-range communication, such as device-to-device (D2D) communication, will become a paramount research focus. D2D communication is severely strained by distinct challenges, such as interference, resource allocation, and security, that degrade its performance in terms of sum rate, resource allocation fairness, and system throughput. In this article, we aim to maximize the overall system throughput of the D2D communication by employing a bio-inspired algorithm - whale optimization algorithm (WOA). It uses whales' social behaviour to detect the optimal D2D users based on their signal-to-noise-plus-interference ratio (SINR) values. Furthermore, we employed an isolation forest algorithm with the intent of identifying potential eavesdroppers who illicitly augment their resources (transmission power) to intercept ongoing D2D communication. The isolation forest algorithm offers anomaly scores that identify deviations in their resources, signalling the presence of entities that may be eavesdropping. The WOA-based access control restricts the eavesdropper and allows only the optimal D2D users to participate in the resource allocation process, thus improving the throughput of the D2D communication. Lastly, the proposed scheme is assessed using different assessment metrics, such as fairness (0.861), system throughput (28.67 Mbps), and convergence rate.

关键词： D2D communication Isolation Forest Resource allocation Access Control whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks

引用

EXPERT SYSTEMS 2025年第1期42卷

作者： Aljanabi, Mohanad R. Borna, Keivan Ghanbari, Shamsollah Obaid, Ahmed J. Kharazmi Univ Fac Math Sci & Comp Tehran Iran Univ Kufa Fac Comp Sci & Math Najaf Iraq Islamic Azad Univ Fac Engn Dept Comp Sci Ashtian Branch Ashtian Iran Kharazmi Univ Fac Math Sci & Comp 15719-14911 Tehran Iran

Multimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi-modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance for cargo transportation, as it enables SC participants to increase logistics efficiency and quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well-suited to resolve complex and nonlinear relationships in the collected tracking data. This study designs a deep learning-powered travel time estimation in multimodal freight transportation networks (DLTTE-MFTN) technique. The goal of the DLTTE-MFTN technique is to estimate the travel time using a hyperparameter-tuned ensemble learning approach. To achieve this, the DLTTE-MFTN method initially undergoes data pre-processing to convert the input raw data into a useful format. In addition, the singular value decomposition (SVD) model can be applied for feature dimensionality reduction in multimodal transport data, considerably improving travel time prediction. Besides, the DLTTE-MFTN method estimates travel time using an ensemble of three DL approaches including one-dimensional convolutional neural network (1D-CNN), stacked autoencoder (SAE) attention, and recurrent neural network (RNN). Finally, the hyperparameter tuning of the DL models takes place using the whale optimization algorithm (WOA). The performance analysis of the DLTTE-MFTN method takes place using the Kaggle dataset. The experimental results stated that the DLTTE-MFTN technique attains superior performance over other ML and DL models.

关键词： deep learning machine learning multi-modal freight transportation singular value decomposition travel time estimation whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Deep learning model for short-term photovoltaic power forecasting based on variational mode decomposition and similar day clustering

引用

COMPUTERS & ELECTRICAL ENGINEERING 2024年 115卷

作者： Li, Meng Wang, Wei He, Yan Wang, Qinghai Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

Short-term photovoltaic power forecasting (PVPF) is crucial in the scheduling and functioning of contemporary electrical systems. A short-term PVPF model based on correlation analysis, similar day clustering, mode decomposition and hybrid deep learning is proposed to address the volatility and stochasticity of PV output. To construct a similar day clustering model, the Kendall rank correlation coefficient is used to select weather features for clustering. Subsequently, the Kmeans++ clustering method is used to divide the dataset into sunny, cloudy, and rainy days, thereby improving the similarity of the training samples. PV power sequences are decomposed into intrinsic mode functions (IMFs) via improved variational mode decomposition (IVMD), reducing non-stationarity and complexity. Introducing the improved whale optimization algorithm (IWOA) for optimal hyperparameters of BiLSTM, and an Attention mechanism is incorporated for effective key information capture. Finally, the IVMD-IWOA-BiLSTM-Attention prediction model is constructed, and the prediction results of each IMF are summed and reconstructed to obtain the final PV power prediction results. Compared with the benchmark model, the hybrid prediction model reduces the RMSE by an average of 86.54 %, 71.01 % and 72.71 % for sunny, cloudy and rainy days, respectively. This approach is of significant importance for decision-making and scheduling in photovoltaic grid connection.

关键词： Photovoltaic power forecasting Variational mode decomposition whale optimization algorithm Deep learning Similar day clustering

来源：评论

学校读者我要写书评

暂无评论

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第8期38卷 3127-3148页

作者： Al-Hasani, Alaa A. Jasim Shahid, Shamsuddin Univ Teknol Malaysia UTM Fac Engn Dept Water & Environm Engn Johor Baharu 81310 Malaysia Minist Agr Dept Agrometeorol Applicat & Climate Change Iraqi Agrometeorol Ctr Baghdad Iraq

Reliable estimation of reference evapotranspiration (ETo), an essential component of optimal irrigation management, is challenging in many regions due to its complex dependence on meteorological factors. Alternative empirical models, often used to estimate ETo considering data limitations, provide highly unreliable estimates for Iraq. This study aimed to formulate simpler empirical models for accurate ETo estimation with fewer variables in different climate regions of Iraq. The metaheuristic whale optimization algorithm (WOA) was used to finetune the coefficients of the nonlinear least square fitting regression (NLLSF) model during development. Two simpler models were developed based on (1) only mean air temperature (T) (NLLSF-T) and (2) solar radiation and T (NLLSF-R) as inputs. The performance of the models was validated using historical ground observations (2012-2021), and the ETo was estimated using the Penman-Monteith method from the reanalyzed (ERA5) datasets (1959-2021). The models' spatial, seasonal, and temporal performance in estimating daily ETo was rigorously evaluated using multiple statistical metrics and visual presentations. The Kling-Gupta Efficiency (KGE) and normalized root mean square error (NRMSE) of the NLLSF-T model were 0.95 and 0.30, respectively, compared to 0.75 and 0.40 for Kharrufa, the best-performing temperature-based models in Iraq. Similarly, NLLSF-R improved the KGE from 0.78 to 0.97 in KGE and NRMSE from 0.44 to 0.22 compared to Caprio, the best-performing radiation-based model in Iraq. The spatial assessment revealed both the models' excellent performance over most of Iraq, except in the far north, indicating their suitability in estimating ETo in arid and semi-arid regions.

关键词： Penman-monteith method whale optimization algorithm Nonlinear regression ERA5 Iraq

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：