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检索条件"主题词=whale optimization algorithms"

共 378 条记录，以下是121-130 订阅

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Particle Swarm optimization-Based Deep Neural Network vs whale optimization Algorithm-Based Deep Convolutional Neural Networks for Critical Heat Flux Prediction

Particle Swarm Optimization-Based Deep Neural Network vs Wha...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Messaoud Djeddou Jehad Al Dallal Aouatef Hellal Ibrahim A. Hameed Xingang Zhao Hydraulic Department Oum El-Bouaghi University Ain Beida Algeria Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait Department of ICT and Natural Sciences Norwegian University of Science and Technology (NTNU) Alesund Norway Department of Nuclear Engineering University of Tennessee Knoxville Knoxville USA

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Critical heat flux (CHF) denotes the thermal limit at which a phase change occurs during heating, that causes a significant decline in heat transfer efficiency and eventual localized overheating of the heating surface. This study applies two hybrid deep learning models: a Deep Convolutional Neural Network (CNN) optimized with the whale optimization Algorithm and a Deep Neural Network (DNN) optimized with Particle Swarm optimization, to predict Critical Heat Flux (CHF). The results show that the latter model outperforms the former in terms of performance, simplicity, and computation efficiency in the CHF prediction task.

关键词： Heating systems Deep learning Training Computational modeling Predictive models Prediction algorithms whale optimization algorithms Convolutional neural networks Particle swarm optimization Heat transfer

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Enhancing the Adaptive Capability of Fuzzy Petri Net by an Improved whale optimization Algorithm

Enhancing the Adaptive Capability of Fuzzy Petri Net by an I...

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Chinese Automation Congress (CAC)

作者： Shao-Qiang Ye Azlan Mohd Zain Yusliza Yusoff Wen-Jie Ruan Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia School of Information Engineering Hunan Applied Technology University Changde China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Fuzzy Petri Nets (FPNs) are an advanced topological model that extends traditional Petri nets (PNs) by integrating fuzzy theory. However, the generalization capability of FPN models is often limited due to their reliance on expert-driven parameter determination. This paper proposes an improved whale optimization algorithm (IWOA) for optimizing FPN models to address this challenge. The IWOA employs an adaptive weighting strategy, optimal search techniques, population grouping, and the Lévy flight strategy to dynamically adjust the search process, enhancing the WOA's effectiveness in handling complex problems and improving the balance between exploration and exploitation. The IWOA is tested on six classical benchmark functions in the experimental section and applied to the FPN model. The results demonstrate that the IWOA-FPN model exhibits strong generalization capabilities and superior convergence performance.

关键词： Adaptation models Automation Heuristic algorithms Petri nets Benchmark testing Search problems whale optimization algorithms Convergence

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Optimal Configuration and Economic Evaluation of Hybrid Energy Storage Using an Improved whale optimization Algorithm

Optimal Configuration and Economic Evaluation of Hybrid Ener...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Yu Chen Liping Guo Chun Yang Jinyu Zhai College of Electric Power Inner Mongolia University of Technology Hohhot Inner Mongolia China Engineering Research Center of Large Energy Storage Technology Ministry of Education Hohhot Inner Mongolia China

ISBN: (数字)9798331516611

ISBN: (纸本)9798331516628

The new distribution network faces the problems of medium and long-period storage difficulties, low carbon and economy to be improved. Optimizing the configuration of hybrid energy storage will help to promote the goal of “double carbon.” The optimal configuration and economic evaluation of the hybrid energy storage composed of lithium battery energy storage and hydrogen energy storage are carried out in order to give full play to the role of energy storage in the new distribution network in this paper. Based on the sensitivity of network losses and the structural characteristics of the distribution network, optimal connection points for the hybrid storage system are identified. A bi-level optimization model is established, incorporating both capital investment and operational costs. The lower-level optimization focuses on minimizing operational costs to determine the optimal energy storage capacity, while the upper-level optimization aims to minimize the overall annual cost of the system. The Topsis method is used to select the Pareto-optimal solutions, yielding the optimal configuration of the hybrid energy storage system's capacity. During the optimization process, the fitness evaluation criterion from the simulated annealing algorithm is integrated into the whale optimization algorithm, which enhances the algorithm's ability to avoid premature convergence and accelerates its convergence speed. An economic evaluation framework for the distribution network is developed, and four different energy storage planning scenarios are assessed using a combination of gray relational analysis and the entropy weighting method. Simulation results based on an improved IEEE 33-node distribution network show that the optimized hybrid energy storage system can reduce line losses, improve voltage quality at nodes, enhance the utilization of distribution transformers, and significantly increase CO 2 and SO 2 emission reductions. Both operational and environmental performance indicat

关键词： Costs Sensitivity Distribution networks Simulated annealing whale optimization algorithms Planning Carbon optimization Energy storage Convergence

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Pollution Flashover Pre-warning Method Based on Beluga whale optimization Algorithm

Pollution Flashover Pre-warning Method Based on Beluga Whale...

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Power and Electrical Technology Conference (APET), Asia

作者： Chaoying Fang Yunchu Cheng Jun Xu Xiaojie Wang Jiafeng Lin Wei Gong Electric Power Research Institute State Grid Fujian Electric Power Co. Ltd. Fuzhou China Fujian Zhongshisuo Electric Power Testing & Commissioning Co. Ltd. Fuzhou China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The problem of pollution flashover of transmission lines seriously interferes with the safety and stability of the power grid system, and insulators occur pollution flashover has a close connection with meteorological conditions. In order to prevent pollution flashover disasters, a pollution flashover pre-warning model considering meteorological data is constructed, and a pre-warning method of the risk of pollution flashover based on the beluga optimization algorithm is proposed. Firstly, the meteorological characteristic quantities are screened according to the fouling mechanism; secondly, the risk assessment model of pollution flashover disaster of transmission lines is constructed based on the meteorological parameters; then, the parameters of the pollution flashover pre-warning model are optimized by the beluga optimization algorithm; finally, an example analysis of line pollution flashover is carried out to verify the effectiveness of the proposed method. The results of the study can provide technical references for power grid anti-pollution flashover work.

关键词： Pollution Rain Power transmission lines Surface contamination Flashover Insulators whale optimization algorithms Power grids Surface treatment optimization

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An Improvement on Exploration Step of whale optimization Algorithm with Levy Distribution for Classification Problems

An Improvement on Exploration Step of Whale Optimization Alg...

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Big Data Analytics and Practices (IBDAP), International Conference on

作者： Sakkayaphop Pravesjit Krittika Kantawong Natdanai Kamkhad Saksit Sabaiporn Jantawan Monchanuan Duangjai Jitkongchuen Arit Thammano Panchit Longpradit School of Information and Communication Technology University of Phayao Phayao Thailand Big Data Instutite Bangkok Thailand Facluty of Information Technology King Mongkut's Institute of Technology Ladkrabang Ladkrabang Thailand Faculty of Social Sciences and Humanities Mahidol University Nakhon Pathom Thailand

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

The proposed system represents an enhanced movement in search of food of whale optimization Algorithm (WOA), based on Levy distribution for image classification of grape leaf disease. In the preprocessing, the proposed system uses convolution kernels to transform images into input data within the range of (0,1). Thereafter, the Levy distribution is incorporated into the WOA model as an exploration search mechanism. A grape leaf dataset from the Plant Village project (***), consisting of 4062 labeled images with dimensions of 256 by 256 pixels and divided into four classes -healthy, Black Rot, Black Measles, and Isariopsis leaf spot -is used to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than Visual Geometry Group (VGG16), Gray Level Co-occurrence Matrix (GLCM) with SVM, Low contrast haze reduction-neighborhood component analysis with SVM, and whale optimization algorithm (WOA).

关键词： Support vector machines Convolution Pipelines Data preprocessing Measles Transforms whale optimization algorithms

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An Improved Beluga whale optimization Algorithm Based on Chaotic Opposition-Based Learning and Adaptive Weighting Factor

An Improved Beluga Whale Optimization Algorithm Based on Cha...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Bingbing Zhang Jingqing Jiang Jiazhi Song College of Mathematics Science Inner Mongolia Minzu University Inner Mongolia China College of Computer Science and Technology Inner Mongolia Minzu University Inner Mongolia China

ISBN: (数字)9798331517021

ISBN: (纸本)9798331517038

Beluga whale optimization (BWO) algorithm faces challenges such as slower convergence, lower accuracy, and instability for dealing with unimodal functions. To tackle these problems, we propose an enhanced version of Beluga whale optimization Algorithm (ABWO). ABWO algorithm incorporates chaotic Opposition-Based Learning (OBL) and adaptive weighting factor. We fuse Logistic and Tent chaotic mappings with OBL to ensure an even distribution of individuals in the initial solution space during initialization. Consequently, the population diversity is increased and the quality of the initial solution is better. The adaptive weighting factor $F$ is introduced in the exploitation phase. It increases and then decreases with each iteration. This adjustment enhances the guiding effect of the global optimal position on individual position updates in the early stage of BWO. It also enables the algorithm to execute more effective global searches in the middle stage. Meanwhile, the algorithm converges more quickly in the late stage. Overall, the solution accuracy and convergence stability of the algorithm are improved. On nine unimodal test functions, ABWO has better optimization and convergence performance compared with six basic algorithms. It also outperforms three improved beluga whale optimization algorithms proposed by other scholars.

关键词： Accuracy Fuses Market research whale optimization algorithms optimization Faces Computational intelligence Convergence Logistics

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Optimal Charge Scheduling and Navigation for Multiple EV Using Deep Reinforcement Learning and whale optimization

Optimal Charge Scheduling and Navigation for Multiple EV Usi...

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Vehicular Technology and Transportation Systems (ICVTTS), International Conference on

作者： Aby N Raj K. Sakthivel Department of EEE Bharath Institute of Higher Education and Research Chennai India

ISBN: (数字)9798350355512

ISBN: (纸本)9798350355529

The demand for Electric Vehicles (EVs) is increasing exponentially in recent times because of its ability to minimize energy savings and carbon emission. However, the charging process and charging option increases the challenges for EV adoption. With the growing adaptability to EVs, the need for addressing the challenges related to limited range and the availability of charging infrastructure becomes crucial. This paper presents an optimized deep learning-based charge scheduling approach in EVs for intelligent transport systems. The study leverages the Deep Reinforcement Learning (DRL) for making real-time decisions. The DRL model is trained using various features such as Battery critical percentage (SOC), time slots, nearest charge station, and availability of charging station. The features are optimized using a nature inspired whale optimization Algorithm (WOA), which helps in obtaining optimal charge scheduling. The proposed approach is experimentally evaluated in terms of reducing the tow counts in the selected region. Results from the experimental analysis validate the efficacy of the proposed approach in achieving optimal charge scheduling and navigation for EVs.

关键词： Protocols Navigation Urban areas Transportation Electric vehicles Deep reinforcement learning whale optimization algorithms Scheduling State of charge optimization

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Big Data Clustering using Ghost Opposition Learning based Crayfish optimization Algorithm

Big Data Clustering using Ghost Opposition Learning based Cr...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yifeng Wang Yuxia Du Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Traditional clustering methods often struggle with the sheer scale and complexity of big data. Clustering algorithm of big data models by efficiently handling large datasets, making them more scalable and capable of processing diverse data types. In this big data Clustering technique-based Swarm intelligence optimization various of model are created but still the optimization algorithms are trapping in local optima and its takes huge time for finding the global solution. Addressing these issues and overcome in this paper, proposed the Crayfish optimization Algorithm by using Ghost Opposition-based Learning (GOL-COA) Strategy and environment update technique for enhancing exploitation phase and increasing Exploration convergence speed of optimization, and avoiding local optima problem. The model is evaluated with the accuracy and F1-score metrices and experiment did on various types of datasets. The numerical validation results showing accuracy of 97.56% in LD dataset, and F1 score of 98.10% in LD dataset. This model compares to the other recent year optimization algorithms such as Particle Swarm optimization (PSO), whale optimization (WOA), Ant Colony optimization (ACO), Sand Cat Swam optimization (SCSO), and Werewolf optimization algorithms (WOA).

关键词： Accuracy Clustering algorithms Big Data Data models whale optimization algorithms Numerical models Particle swarm optimization Computer security optimization Convergence

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Improved Energy Balancing based Data Forwarding incorporating whale optimization Technique in Underwater Acoustic Sensor Network

Improved Energy Balancing based Data Forwarding incorporatin...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： R. Bhairavi M. Thiruveni Anita Christaline J N. Ashokkumar I. Chandra P. Nagarajan Department of ECE SRM Institute of Science and Technology Chennai India Department of ECE PSNA College of Engineering and Technology Dindigul India School of Computer Science and Engineering VIT Chennai India Department of Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati Andhra Pradesh India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

UWASN has various challenges such as minimal bandwidth availability, long propagation delay, increased energy exhaustion, increased deployment cost and security. The routing protocols should facilitate an enhanced data forwarding towards sink nodes. In this paper, an Improved Energy Balancing based Data Forwarding incorporating whale optimization Technique (IEBDF-WO) is proposed to effectively elect optimal forwarders to participate in the routing process. The whale optimization algorithm has three phases namely, scouring phase, encompassing phase and final bubble net raiding phase for facilitating enhanced data transmission towards sink node. The proposed technique determines a fitness function by using the weighted depth difference W D , Potential Relay Count (PRC), Residual energy of each potential relay node. The performance of the proposed IEBDF-WO technique is analyzed and is collated with the existing Energy-Efficient Probabilistic Depth-Based Routing (EEPDBR) technique. It is seen that, for a network comprising of 200 nodes, the proposed scheme has improved Packet Delivery Ratio of 96.04% while minimizing the end to end delay, energy tax and average number of hops.

关键词： Electric potential Routing Probabilistic logic whale optimization algorithms Routing protocols Security Relays Underwater acoustics optimization Propagation delay

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An Overview of optimization Challenges in Electric Mobility Charging Stations and Driver Behavior in Congested Cities

An Overview of Optimization Challenges in Electric Mobility ...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Juliana Bermúdez Juan Esteban González David Celeita School of Engineering Science and Technology Universidad del Rosario Bogotá Colombia Faculty of Engineering Universidad de la Sabana Chía Colombia

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

As the electric transportation industry expands, is imperative to face the implications of this new infrastructure. In fact, this paper intends to generate a comprehensive overview of the challenges associated with the implementation of an electric vehicle charging system in Colombia’s capital city Bogotá. Given the complexity of the problematic, it is divided into five main components: optimal location for charging stations, electric vehicles’ routing algorithms, scheduling and queue management, driver’s behavior and modeling of dynamical systems. This division, enables the analysis of methodologies such as swarm optimization algorithms, genetic-based algorithms, whale optimization algorithms, spherical fuzzy sets, Voronoi diagrams, dynamic weighted graphs, Lyapunov functions, generalized linear models, convolutional networks, Markov chains and some others in terms of how appropriate they are for providing a solution. Finally, a preliminary model to represent this dynamic is introduced along as further work taking as a starting point the representation of the system.

关键词： Heuristic algorithms Transportation industry Urban areas Charging stations Routing whale optimization algorithms Smart grids

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