检索结果-内蒙古大学图书馆

Journal of Network Intelligence 2025年第1期10卷 299-311页

作者： Li, Jian-Fan Li, Zhong-Wu Fu, Arends College of Transportation Engineering Tongji University Shanghai201804 China College of Big Data Baoshan University Baoshan678000 China College of Engineering and Information Technology Cavite State University Lahug Cebu6000 Philippines

Short-term traffic flow prediction is an important research area in smart transportation systems. To enhance prediction accuracy, scholars have been exploring different combination models. Among these models, the wavelet neural network stands out as it combines wavelet analysis and neural networks, offering improved results in short-term traffic flow forecasting. The tunicate group optimization algorithm is another promising technique, characterized by its simplicity, efficiency, and minimal parameters required. In this study, we introduce the tunicate swarm algorithm to adjust the weights and wavelet factors of the network in the Wavelet Neural Networks (WNN) model, aiming to enhance its prediction accuracy and overall performance. We first apply wavelet analysis to distill the key features of the traffic flow data. Next, we construct the WNN model and input the traffic flow data, along with data from previous moments, for training and learning. Subsequently, we optimize the network’s weights and wavelet factors using the tunicate swarm algorithm (TSA), further enhancing the model’s performance. Experimental findings showcase that our suggested technique attains remarkable precision and consistency in short-term traffic flow forecasting. Thus, this method holds significant value in traffic management and planning, providing practical applications for improving the reliability and accuracy of traffic flow forecasting. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Short traffic flow prediction tunicate swarm algorithm Wavelet neural network

来源：评论

学校读者我要写书评

暂无评论

tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

引用

JOURNAL OF ENGINEERING DESIGN AND TECHNOLOGY 2022年第5期20卷 1172-1187页

作者： Ajayi, Oluwafemi Heymann, Reolyn Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa Univ Johannesburg Ctr Collaborat Digital Networks Johannesburg South Africa

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC's hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model's input parameters on the DC's energy demand pattern. Findings The performance of the proposed model has been compared with

关键词： Machine learning Data centre Energy demand prediction tunicate swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

tunicate swarm algorithm Based Difference Variation Flower Pollination algorithm 18th

Tunicate Swarm Algorithm Based Difference Variation Flower P...

引用

18th International Conference on Intelligent Computing (ICIC)

作者： Yu, Chuchu Huang, Huajuan Wei, Xiuxi Guangxi Minzu Univ Coll Elect Informat Nanning 530000 Peoples R China Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530000 Peoples R China

ISBN: (纸本)9783031138706;9783031138690

Flower pollination algorithm is a novel meta-heuristic swarm intelligence optimization algorithm, for its problems of insufficient solution accuracy, slow convergence speed and low stability, the tunicate swarm algorithm based difference variation flower pollination algorithm (TSA-DVFPA) is proposed in this paper. The simplified tunicate swarm algorithm and the random selection strategy were introduced into the process of cross-pollination. The differential variation strategy has been applied to the local search of the algorithm iteration to increase the diversity of the population. The improvement is validated by 16 benchmark functions. Compared with other similar algorithms, the results show that the proposed algorithm has a certain improvement in the convergence speed and the accuracy of optimization.

关键词： Flower pollination algorithm tunicate swarm algorithm Difference -variation Function optimization

来源：评论

学校读者我要写书评

暂无评论

Modified tunicate swarm algorithm-based methodology for enhancing the operation of partially shaded photovoltaic system

引用

ALEXANDRIA ENGINEERING JOURNAL 2023年 79卷 449-470页

作者： Fathy, Ahmed Amer, Dina A. Al-Dhaifallah, Mujahed Jouf Univ Fac Engn Dept Elect Engn Sakaka 72388 Saudi Arabia Zagazig Univ Fac Engn Dept Elect Power & Machines Zagazig 44519 Egypt Higher Technol Inst Comp Sci Dept Tenth Of Ramadan City 44228 Egypt King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Renewable Energy & Power Syst IRC REPS Dhahran 31261 Saudi Arabia

The enhancement of photovoltaic array conversion efficiency during the partial shade operation is essential as the power-voltage curve includes multi-local peaks and one global peak. In such case, the conventional hill climbing-based maximum power point tracker approaches failed in monitoring the global power as they fall in local optima. Therefore, this paper proposes a new metaheuristic approach named modified tunicate swarm algorithm to track the global power of the photovoltaic array in the event of partial shade operation. In the proposed approach, two distinct random numbers are incorporated in the tunicate's updating process to enhance the search ability and avoid falling in local optima. The proposed approach is validated using two applications, CEC'17 test suite and practical one of designing the tracker installed with the photovoltaic array operated at partial shade. The proposed approach controls the dc-dc boost converter at the array terminals via obtaining the suitable duty cycle at which the global power is extracted. Five shade patterns with distinct locations of global power are analyzed in addition to normal operation, the proposed tracker is compared to teaching-learningbased optimizer, particle swarm optimizer, Coot optimization algorithm, capuchin search algorithm, and perturb and observe. Moreover, statistical tests of ANOVA table, Wilcoxon rank test, and Friedman test are conducted to assess the proposed approach. The fetched results clarified that, the best power error obtained via the proposed tracker is 0.000042 W at the third shade pattern while the worst one is 0.0652 W at the fifth pattern. Moreover, the p-value obtained via ANOVA table is 1.85399e-19 which confirms significant difference in the column means. The results proved the superiority and competence of the proposed tracker in solving the CEC'17 test suite and extracting the best global power from the photovoltaic array in all studied operating conditions.

关键词： tunicate swarm algorithm Partial shade Photovoltaic Maximum power point tracker

来源：评论

学校读者我要写书评

暂无评论

Optimal Multi-Microgrids Energy Management Through Information Gap Decision Theory and tunicate swarm algorithm

引用

IEEE ACCESS 2024年 12卷 114795-114808页

作者： Rashidi, Reza Hatami, Alireza Moradi, Mansour Liang, Xiaodong Bu Ali Sina Univ Fac Engn Dept Elect Engn Hamadan *** Hamadan Iran Natl Univ Skills Dept Elect Engn Tehran *** Iran Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

A multi-microgrid (MMG) consists of several individual microgrids (MGs) within a distribution system to improve the system's stability and reliability. A MMG can operate in grid-connected or island mode and requires advanced control techniques and effective energy management. This paper proposes a novel energy management approach for a MMG at the tertiary level control (TLC) using an adaptive optimal control model. Operational costs of the MMG are minimized for short-term planning while satisfying operational constraints of the network;the influential indices, the energy not supplied (ENS) and fatigue life (FL), remain balanced. The information gap decision theory (IGDT) is used to consider uncertainties in power generation and consumptions. MATLAB and DigSilent are used simultaneously to model optimally connected individual MGs within a MMG. The tunicate swarm algorithm (TSA) is used for TLC for cost calculation and forming optimal connection models of individual MGs. The proposed method is validated through several case studies, showing superior performance.

关键词： Energy management fatigue life information gap decision theory multi-microgrid tertiary-level control tunicate swarm algorithm fatigue life information gap decision theory multi-microgrid tertiary-level control tunicate swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Opposition-Based tunicate swarm algorithm for Parameter Optimization of Solar Cells

引用

IEEE ACCESS 2021年 9卷 125590-125602页

作者： Sharma, Abhishek Sharma, Abhinav Dasgotra, Ankit Jately, Vibhu Ram, Mangey Rajput, Shailendra Averbukh, Moshe Azzopardi, Brian Univ Petr & Energy Studies Dept Res & Dev Dehra Dun 248007 Uttarakhand India Univ Petr & Energy Studies Dept Elect & Elect Engn Dehra Dun 248007 Uttarakhand India Inst Engn & Transport MCAST Energy Res Grp Malta Coll Arts Sci & Technol MCAST Paola 9032 Malta Graph Era Dept Math Comp Sci & Engn Dehra Dun 248002 Uttarakhand India Peter Great St Petersburg Polytech Univ Inst Adv Mfg Technol St Petersburg 195251 Russia Ariel Univ Dept Elect & Elect Engn IL-40700 Ariel Israel

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum (6.83 x 10(-4), 2.06 x 10(-4), and 4.48 x 10(-6)) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

关键词： Machine learning parameter extraction photovoltaic cells metaheuristics tunicate swarm algorithm opposition-based learning

来源：评论

学校读者我要写书评

暂无评论

A Novel tunicate swarm algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment

引用

IEEE ACCESS 2022年 10卷 127192-127204页

作者： Taher, Fatma Elhoseny, Mohamed Hassan, Mohammed K. El-Hasnony, Ibrahim M. Zayed Univ Coll Technol Innovat Dubai U Arab Emirates Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates Mansoura Univ Fac Comp & Informat Mansoura 35516 Egypt Horus Univ Egypt HUE Fac Engn Mechatron Dept Dumyat 34518 Egypt

The Internet of Things (IoT) paradigm has matured and expanded rapidly across many disciplines. Despite these advancements, IoT networks continue to face an increasing security threat as a result of the constant and rapid changes in the network environment. In order to address these vulnerabilities, the Fog system is equipped with a robust environment that provides additional tools to beef up data security. However, numerous attacks are persistently evolving in IoT and fog environments as a result of the development of several breaches. To improve the efficiency of intrusion detection in the Internet of Things (IoT), this research introduced a novel tunicate swarm algorithm that combines a long-short-term memory-recurrent neural network. The presented model accomplishes this goal by first undergoing data pre-processing to transform the input data into a usable format. Additionally, attacks in the IoT ecosystem can be identified using a model built on long-short-term memory recurrent neural networks. There is a strong correlation between the number of parameters and the model's capability and complexity in ANN models. It is critical to keep track of the number of parameters in each model layer to avoid over- or under-fitting. One way to prevent this from happening is to modify the number of layers in your data structure. The tunicate swarm algorithm is used to fine-tune the hyper-parameter values in the Long Short-Term Memory-Recurrent Neural Network model to improve how well it can find things. TSA was used to solve several problems that couldn't be solved with traditional optimization methods. It also improved performance and shortened the time it took for the algorithm to converge. A series of tests were done on benchmark datasets. Compared to related models, the proposed TSA-LSTMRNN model achieved 92.67, 87.11, and 98.73 for accuracy, recall, and precision, respectively, which indicate the superiority of the proposed model.

关键词： Internet of Things Security Organizations Intrusion detection Recurrent neural networks Deep learning Behavioral sciences Data security deep learning fog computing internet of things intrusion detection tunicate swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization

引用

HEALTH INFORMATION SCIENCE AND SYSTEMS 2024年第1期12卷 42页

作者： Bhimavarapu, Usharani Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada Andhra Prades India

Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy. This study proposes an automated detection method using fundus images. Image segmentation divides fundus images into homogeneous regions, facilitating feature extraction. Feature selection aims to reduce computational costs and improve classification accuracy by selecting relevant features. The proposed algorithm integrates an Improved tunicate swarm algorithm (ITSA) with Renyi's entropy for enhanced adaptability in the initial and final stages. An Improved Hybrid Butterfly Optimization (IHBO) algorithm is also introduced for feature selection. The effectiveness of the proposed method is demonstrated using retinal fundus image datasets, achieving promising results in DR severity classification. For the IDRiD dataset, the proposed model achieves a segmentation Dice coefficient of 98.06% and classification accuracy of 98.21%. In contrast, the E-Optha dataset attains a segmentation Dice coefficient of 97.95% and classification accuracy of 99.96%. Experimental results indicate the algorithm's ability to accurately classify DR severity levels, highlighting its potential for early detection and prevention of diabetes-related blindness.

关键词： tunicate swarm algorithm Renyi's entropy Butterfly Optimization algorithm Dice coefficient

来源：评论

学校读者我要写书评

暂无评论

SLOTSA: A Multi-Strategy Improved tunicate swarm algorithm for engineering constrained optimization problems

SLOTSA: A Multi-Strategy Improved tunicate swarm algorithm f...

引用

1st IEEE International Conference on Software Services Engineering (IEEE SSE)

作者： Wang, Wentao Fan, Chengshuai Pan, Zhongjie Tian, Jun Nankai Univ Coll Software Tianjin Peoples R China

ISBN: (纸本)9798350340754

With the increasing demands on industrial products, the design and manufacturing problems of industrial products are becoming more and more complex, Many industrial design problems belong to nonlinear optimization problems and NP-hard problems, such as shop scheduling, path planning, and industrial part design problems, In the last decade, more and more researchers have developed heuristic-based algorithms to deal with industrial design problems, Tnnicate swarm algorithm (fSA) is a newly proposed high performance heuristic optimization algorithm, The TSA algorithm has the problems of low solution accuracy, slow convergence and easy attraction by local extrema, In order to solve some drawbacks of TSA, this paper improves TSA and proposes the Sine cosine -Levy-Opposition-based Tnnicate swarm algorithm (SLOTSA) by combining opposition-based learning, Levy flight and positive cosine operator, Comparative experiments on SLOTSA in 10 benchmark functions fully demonstrate the rationality of the three improvement strategies, In addition, this paper applies SLOTSA to two practical industrial design problems, and the optimization results demonstrate the wide applicability of SLOTSA to industrial design problems,

关键词： tunicate swarm algorithm Industrial design problems Heuristic algorithms Opposition-based learning strategy Engineering Problem Design

来源：评论

学校读者我要写书评

暂无评论

An improved tunicate swarm algorithm with random opposition based learning for global optimization problems

引用

OPSEARCH 2024年 1-26页

作者： Chandran, Vanisree Mohapatra, Prabhujit Vellore Inst Technol Math Vellore 632014 Tamil Nadu India

The tunicate swarm algorithm (TSA) is a recently introduced bio-inspired optimization algorithm motivated by the foraging and swarming behaviour of bioluminescent tunicates. It has gained a lot of attention from the heuristic community because of its superior performance in solving various optimization problems. However, it is also easy to get stuck in the local optima, resulting in premature convergence when dealing with highly challenging optimization problems. To alleviate these shortcomings, this study presents an improved TSA termed random opposition based TSA (ROBTSA), which integrates a novel random opposition based learning (ROBL) technique into the conventional TSA. This proposed approach is implemented with jumping probability, which facilitates the algorithm to jump out of local optimal traps by enhancing the diversity of the tunicates. To test the efficacy of the proposed algorithm, experimentations are conducted on a set of thirteen standard test functions, comprising unimodal and multimodal functions. The proposed ROBTSA is tested against several well-known and advanced algorithms, including PSO, GWO, WOA, SCA, MVO, and STOA. In addition, it has been compared with the original TSA and its variant OBTSA. Further, it has been applied to solve two real-life engineering design problems: pressure vessel and tension/compression spring problems. The experimental outcomes exhibit that ROBTSA outperforms the other competing algorithms in terms of convergence rate, accuracy, and stability. Moreover, the performance of ROBTSA has been proven by statistical measures such as the Friedman test and Wilcoxon rank-sum test, demonstrating its potential in the realms of global optimization and real-life engineering design problems.

关键词： tunicate swarm algorithm Random opposition based learning Meta-heuristic algorithms Global optimization Engineering design problems

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：