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检索条件"主题词=Whale Optimization Algorithms"

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Shanghai Port Container Throughput Prediction Based on EEMD-WOA-LSTM

Shanghai Port Container Throughput Prediction Based on EEMD-...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Mengyuan Ding Feng Liu Department of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Given the important role of container throughput prediction in port management and development, this paper proposes a combined prediction model based on EEMD-WOA-LSTM. The model first uses the Ensemble Empirical Mode Decomposition (EEMD) technique to decompose monthly container throughput data into multiple intrinsic mode functions (IMF) and trend residuals with different characteristics. Then, it utilizes a Long Short-Term Memory (LSTM) network optimized by whale optimization Algorithm (WOA) to predict each component. Finally, the predictions of all components are integrated to obtain the final forecast value. Empirical results show that the proposed combined prediction model significantly outperforms traditional models in terms of prediction accuracy and stability, demonstrating good application prospects. This model can provide scientific evidence and decision support for relevant port authorities and shipping enterprises.

关键词： Accuracy Empirical mode decomposition Containers Predictive models Throughput Prediction algorithms Market research Data models whale optimization algorithms Long short term memory

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Research on Security Risk Constructs in Attack Detection and Defense Schemes for Smart Grid Systems

Research on Security Risk Constructs in Attack Detection and...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Zhenqi Wang Liang Wang Zhe Yu State Grid Benxi Electric Power Supply Company Benxi Liaoning China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study proposes an AI-ADP system for the Smart Grid (SG) framework to address the increasing vulnerabilities to cyberattacks and threats in the advanced digital grid system. The AI-ADP system is an Artificial Intelligence (AI)-based Attack Detection and Prevention (ADP) tool that uses a cryptography-based recommendation system to ensure data security and integrity. The system is divided into two stages: trap detection and snare prevention. The WOA-ANN (whale optimization Algorithm-Artificial Neural Network) tool is used for trap detection and classification, while SHA-512 is used for secure communication. Evaluation results show that the proposed AI-ADP system outperforms existing methods and achieves a high accuracy of 99.12% in detecting and preventing cyberattacks in the SG framework.

关键词： Accuracy Prevention and mitigation Instruments Knowledge based systems whale optimization algorithms Smart grids Cryptography Internet of Things Computer crime Recommender systems

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Research on Combined Ultra-short-term PV Power Prediction Method Based on VMD-WOA-BiLSTM

Research on Combined Ultra-short-term PV Power Prediction Me...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Jian Cui Yanfeng Liu Zekang Ma Bowei Xu Yanfei Huang Daixuan Zhou Yan Jia North China Branch of China Petroleum Engineering & Construction Co. Ltd. Renqiu China College of Energy and Power Engineering Inner Mongolia University of Technology Hohhot China College of Electric Power Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798331521844

ISBN: (纸本)9798331521851

Photovoltaic (PV) power generation offers significant environmental benefits and is one of the effective means to address environmental issues fundamentally. In the operation of PV power plants, accurate power prediction technology is crucial for daily scheduling and power generation planning. To address the challenges of high randomness and low prediction accuracy in PV power forecasting, this study proposes a PV power prediction model based on Variational Mode Decomposition (VMD), whale optimization Algorithm (WOA), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. To tackle the issue of high volatility in PV power output data, the VMD data decomposition algorithm is employed to decompose the original power data, thereby enhancing the reliability of the raw data and reducing the impact of noise in the dataset. Additionally, to overcome the problems of large prediction biases and low accuracy caused by improper selection of hyperparameters in traditional prediction models, the WOA is utilized to optimize the hyperparameters of the BiLSTM prediction model, thus improving the reliability of the prediction model. Finally, the proposed model is validated through case simulations and compared with control groups. The results demonstrate the practicality and effectiveness of the proposed method.

关键词： Photovoltaic systems Accuracy Uncertainty Noise Bidirectional long short term memory Predictive models Prediction algorithms whale optimization algorithms Reliability optimization

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A Multi-Optimizer Metaheuristic Approach for Solving Constrained Patch Scheduling Problem

A Multi-Optimizer Metaheuristic Approach for Solving Constra...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Louay Karadsheh Faten Hamad Hussam Fakhouri Faculty of Information Technology University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman The University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall performance. In this work, we optimize the problem of constrained patch scheduling using a multi-optimizer metaheuristic approach. We first model patch scheduling as an optimization problem aiming to minimize both total downtime and vulnerability exposure while also respecting concurrency limits, which restrict the number of machines simultaneously offline. The objective function incorporates a penalty factor to discourage over-concurrent patching. We compare eight modern metaheuristic algorithms with variable patch durations. Extensive experiments are conducted, analyzing best objective scores, downtime, exposure, concurrency penalty, computational time, and convergence trends. **Our findings indicate that Equilibrium Optimizer (EO) achieves the lowest final cost (6.5351), followed closely by Multi-Verse Optimizer (MVO) (6.8295). Whereas Grey Wolf Optimizer (GWO) and Moth Flame optimization (MFO), yield moderate results, while whale optimization Algorithm (WOA) exhibits the highest cost (10.3944). These outcomes underscore the impact of concurrency-limited patching constraints on performance and emphasize the benefits of selecting optimizers that effectively balance exploration and exploitation for scheduling in security-critical environments.

关键词： Concurrent computing Costs Processor scheduling Metaheuristics Fires Market research Linear programming whale optimization algorithms Security Convergence

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Optimized Cross-Layer Cross-Domain Routing for Cluster-Based Dense Wireless Sensor Networks

Optimized Cross-Layer Cross-Domain Routing for Cluster-Based...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Subhashini Peneti Jitendra Prithviraj Deepak Patidar Sanjay Kumar Suman R. Poornima Gargi Verma Department of CSE MLR Institute of Technology Hyderabad Dept of CSE Gyan Ganga Institute of Technology & Sciences Jabalpur MP India Department of Electronics & Communication Engineering IES College of Technology Bhopal M.P. India Dept. of ECE and Dean R&D St. Martin's Engineering College Secunderabad Telangana India Department of CSE-AI&ML School of Engineering Malla Reddy University GGITS MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

One of the most pressing issues in Wireless Sensor Networks (WSN) is finding a way to extend the life of the networks. In order to save the limited energy assets of Wireless Sensors Network, it is essential to model efficient approaches. The use of cross-layer procedures, which aim to optimize routing while also increasing the lifespan of the network, is a solution to these problems. In this paper, we provide a novel clustering-based Cross-Layer Cross-Domain Forwarding model. An innovative cross-layer cross-domain route methodology is used to discuss and improve a cluster-based heavy wireless network of sensors. To improve the functionality of complex wireless sensors, we presented a new hybrid method that combines elements of the Gray Wolf and whale Optimize algorithms. In the outcomes section, we compare the suggested model's performance to that of six additional typical models; we also look at the life span of the entire network as a whole and analyse the alive nodes for typical, developed, and absolutely advanced nodes in three separate test cases that vary in cluster size. Results for network lifetime and alive nodes assessment are better with the suggested technique compared to the current method. The suggested Adaptive Weighted whale optimization Algorithm (AW-WOA) consistently exceeds the other approaches, with an accuracy of 95% Grey Wolf optimization (GWO), whale optimization Algorithm (WOA), and Bat Search optimization (BSO). This is the case when compared to the alternative methods. On the other hand, AW-WOA retains outstanding outcomes, displaying improved stability and efficacy in optimisation throughout all phases, while all other approaches see a drop in accuracy with time.

关键词： Cross layer design Wireless sensor networks Analytical models Accuracy Clustering algorithms Routing whale optimization algorithms Sensors optimization Standards

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Research on Noise Reduction Methods for Wheel Force Sensor Signals

Research on Noise Reduction Methods for Wheel Force Sensor S...

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Sensors and Information Technology (ICSI), International Conference on

作者： Junjie Yu Huibin Cao Qiang Li Yuxiang Sun Hu Xu School of Electronic and Information Engineering Anhui Jianzhu University Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Big Data and Internetline Shenzhen Technology University Shenzhen China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

The wheel force data acquisition system for six-component force measurements often encounters noise signals during the data acquisition process. If these noises are not removed, they can severely impact the accuracy of vehicle performance analysis. This paper proposes a noise reduction method (IWOA-VMD-IWT) based on an improved whale optimization algorithm combined with variational modal decomposition and improved wavelet thresholding. By using IWOA to optimize the VMD parameters, the sensor signal is decomposed into intrinsic mode functions (IMFs). These IMFs are then classified into noise-dominant and signal-dominant components based on similarity results. The noise-dominant components undergo the improved wavelet thresholding (IWT) process, and the noise-reduced signal is obtained through final reconstruction. The experimental results indicate that the SNR exceeds 14dB and the RMSE is below 0.17. This represents an improvement of over 8dB in SNR and a reduction of more than 0.14 in RMSE compared to traditional wavelet threshold noise reduction methods. These findings demonstrate the feasibility of the proposed method in vehicle motion analysis.

关键词： Accuracy Noise reduction Data acquisition Wheels whale optimization algorithms Space exploration Performance analysis Force sensors Root mean square Signal to noise ratio

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Ultra-Short-Term Prediction Method of Photovoltaic Power Generation Based on Improved GRNN-LSTM Combination Model

Ultra-Short-Term Prediction Method of Photovoltaic Power Gen...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yun Wu Tianyang Li Jieming Yang Dan Feng School of Computer Science Northeast Electric Power University China Chaoyang City Ecological and Environmental Affairs Service Center Chaoyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Ultra-short-term prediction of photovoltaic power generation is the prerequisite for real-time dispatch of the power system, in order to further improve the accuracy of ultra-short-term prediction of photovoltaic power, this paper proposes an ultra-short-term prediction method of photovoltaic power based on an improved Generalized Regression Neural Network(GRNN)-Long Short-Term Memory (LSTM) combination model. Firstly, in the GRNN model, an improved whale optimization algorithm based on grid constraints, quasi-reverse learning and local perturbations is proposed to solve the optimal smoothing factor of the model and improve the prediction accuracy. Secondly, in the LSTM model, a CNN convolutional layer and attention mechanism are introduced to better exploit temporal information and enhance the predictive capabilities for complex weather changes. Finally, a combination model threshold setting method based on an improved Dynamic Time Warping (DTW) algorithm is proposed. This method dynamically selects the best model's prediction results based on thresholds during different time periods, fully leveraging the advantages of each model to meet the diverse weather conditions in photovoltaic power prediction. Experimental results demonstrate that the improved GRNN model excels in predicting sunny weather power, the improved LSTM model excels in predicting complex weather power, and the combination model performs well across various meteorological conditions, providing a robust solution for photovoltaic power ultra-short-term prediction problems.

关键词： Photovoltaic systems Accuracy Heuristic algorithms Predictive models Power system stability Prediction algorithms whale optimization algorithms

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State of Health Estimation for Lithium-Ion Batteries Based on Improved Support Vector Regression

State of Health Estimation for Lithium-Ion Batteries Based o...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Xuelei Xia Yue Peng Jingyuan Hu Jiangwei Shen Zheng Chen Faculty of Transportation Engineering Kunming University of Science and Technology Kunming China

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

To solve the problems with imprecise aging feature extraction and time-consuming parameter optimization in current data-driven lithium-ion battery state of health (SOH) estimation algorithms, an improved support vector regression (SVR) algorithm is proposed for accurate SOH prediction. First, the aging data of lithium-ion batteries are preprocessed using incremental capacity curves to extract key factors representing the battery’s aging state, which are then validated through grey relation analysis (GRA). Subsequently, the enhanced whale optimization algorithm (EWOA) is employed to optimize the kernel function parameters of the SVR algorithm. This enables the accurate estimation of the battery’s SOH based on the optimized SVR model. Finally, the model’s accuracy is validated by comparing it with traditional support vector machines (SVM), gaussian process regression (GPR), and extreme learning machine (ELM). The results indicate that the optimized SVR model developed in this study is capable of accurately estimating the SOH of the battery, with a maximum estimation error of less than 3%.

关键词： Support vector machines Lithium-ion batteries Accuracy Estimation Aging Feature extraction Prediction algorithms whale optimization algorithms Data models optimization

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Photovoltaic Power Generation Prediction Based on IWOA-TCN-Self-Attention-LSTM Neural Network

Photovoltaic Power Generation Prediction Based on IWOA-TCN-S...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Junming Wang Zhiheng Hu Zhenzhen Hu School of Communication Engineering Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Addressing the challenge of predicting photovoltaic power generation, which is hampered by various meteorological factors that disrupt the power output, remains a difficult task. This paper presents a fusion model (IWOA- TCN-Self-Attention-LSTM) based on improved whale optimization algorithm, self-attention mechanism, time convolution neural network and long short-term memory neural network. Firstly, Pearson correlation coefficient is used to select which are more important influencing factors from weather factors. Then, the optimized hyperparameters of the fusion model is carried out by using IWOA; Finally, the optimized hyperparameters are employed in the fusion model for training, leading to the generation of the final prediction result. And the prediction results show that the fusion model has better prediction accuracy.

关键词： Photovoltaic systems Training Analytical models Accuracy Neural networks Time series analysis Predictive models Prediction algorithms whale optimization algorithms Reliability

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A BWO-VMD-CNN-BiLSTM-Attention Model for HF Communication foF2 Prediction in the Chinese Middle Latitude Region

A BWO-VMD-CNN-BiLSTM-Attention Model for HF Communication fo...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Zhenhai Lu Kun Xu Hansheng Wang Ke Du College of Information and Communication National University of Defense Technology Wuhan China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The critical frequency in the F2 layer (foF2) is an important parameter in the HF communication, for the reason that F2 layer can reflect shortwave radio waves. In this paper, we consider using the optimized signal analysis method combined with deep neural network to predict the foF2 sequence changes of nine links in the middle latitude region of China. The combined model used in this paper is composed of Variational Mode Decomposition (VMD) under the Beluga whale optimization (BWO) algorithm, Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM) neural network, and attention mechanism layer. Variational mode decomposition is used to reduce the impact of non-stationary features on prediction, and CNN and BiLSTM combine with the attention mechanism layer to extract the change features in the foF2 sequence. By using the historical ionospheric information of 9 links to train the implemented model, that is BWO-VMD-CNN-BiLSTM-Attention model, the results show that the implemented model reduces the prediction error (RMSE) by 21.72 % and 23.71 % respectively compared with the CNN-BiLSTM-Attention model and the BiLSTM model, and the prediction performance is improved under different detection links.

关键词： Attention mechanisms Bidirectional long short term memory Signal processing algorithms Predictive models Feature extraction Prediction algorithms whale optimization algorithms Convolutional neural networks Signal analysis

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