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检索条件"主题词=whale optimization algorithms"

共 386 条记录，以下是281-290 订阅

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CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Communication

CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Commun...

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

作者： Vaishali Aggarwal Kashvi Goel Ishika Gupta Prerna Ajmani School of Information Technology Vivekananda Institute of Professional Studies-Technical Campus Delhi India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Wireless sensor networks (WSNs) and their applications are on an increasing trend owing to the users’ demands with interest in environmental activities, intelligent cities, and medical assistance. However, because of the inefficiency and limitations of wireless sensor networks, it is still very difficult to optimize the network parameters and performance (energy consumption, area coverage and reliable information transfer). In this paper optimization algorithms including Ant Colony optimization (ACO), Moth Flame optimization (MFO) and whale optimization Algorithm (WOA) to enhance the effectiveness of wireless sensor systems are presented. In this approach, ACO captures and explores functional properties, MFO coordinates the optimization and exploration processes, and WOA concentrates on global exploration. Wide-ranging simulations demonstrate that the recommended approach is more effective than the existing approaches in enhancing energy efficiency, network lifetime and effectiveness of packet delivery. The findings in this paper indicate that the proposed hybrid algorithms support the various requirements of the WSN, thus promoting development of networks that are secure and dependable.

关键词： Wireless sensor networks Energy consumption Smart cities Green communications Power control Interference Routing whale optimization algorithms Reliability optimization

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Improved Marine Predators Algorithm for Energy Efficient Placement of UAV-BSs

Improved Marine Predators Algorithm for Energy Efficient Pla...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Rachda El Taani Karim Baiche Yassin Meraihi Sylia Mekhmoukh Abdelbaki Benayad LIST Laboratory University of M’ Hamed Bougara Boumerdes Avenue of Independence Boumerdes Algeria LIMOSE Laboratory University of M’ Hamed Bougara Boumerdes Avenue of Independence Boumerdes Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

Drones operating as flying base stations, or UAV-BSs, are receiving a lot of interest as an appropriate strategy for mobile communication, especially for emergencies. When terrestrial base stations malfunction or there are unexpected increases in demand, UAVs can be quickly deployed to provide continuous service. To improve downlink coverage efficiency and minimize energy consumption, this research discusses the state of positioning UAV-BSs optimally and taking into account both user distribution and drone altitude. An improved version of the Marine Predators Algorithm (MPA) is introduced to solve this NP-hard issue that integrates elements from the original MPA, incorporating the concept of chaotic maps and the Opposition-Based Learning (OBL) mechanism. In several scenarios, our results show that, compared to the original MPA, the whale optimization algorithm (WOA), and the Grey Wolf optimization (GWO) algorithms, the improved MPA is a reliable and effective method for placing UAVs in static situations.

关键词： Wireless communication Base stations Chaotic communication Simulation Mobile communication whale optimization algorithms Energy efficiency Reliability optimization Drones

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Enhancing Fine-Tuning of Pre-trained Language Models for Sentiment Analysis Using Metaheuristic algorithms

Enhancing Fine-Tuning of Pre-trained Language Models for Sen...

引用

International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Pranav T Pattanashetty Pratyush Pankaj Rishika Kinger Rehan Sayed Arti Arya Richa Sharma Department of CSE PES University Bengaluru India

ISBN: (数字)9798331518127

ISBN: (纸本)9798331518134

Fine-tuning pre-trained language models for specific natural language processing tasks often leads to suboptimal performance due to the vast parameter space and the challenge of finding the most effective model configurations. Traditional fine-tuning methods can be computationally expensive and may not fully exploit the potential of these models, particularly for tasks like sentiment analysis. This work explores the application of metaheuristic techniques, focusing on sentiment analysis using the Stanford Sentiment Treebank 2 (SST2) *** utilize two approaches: Genetic algorithms (GA) and the whale optimization Algorithm (WOA). These methods aim to efficiently search the parameter space and optimize the fine-tuning process of pretrained language models. To enhance efficiency, we incorporate layer freezing techniques along with different crossover strategies. In the results obtained, GA with Simulated Binary Crossover (SBX) and 30% layer freezing achieved 94.02% accuracy, while WOA with SBX crossover and 30% layer freezing achieved 94.67% accuracy. These outcomes highlight the potential of integrating evolutionary algorithms, nature-inspired optimization techniques, and strategic layer freezing with deep learning finetuning processes for NLP tasks.

关键词： Adaptation models Sentiment analysis Analytical models Accuracy Heuristic algorithms Computational modeling Metaheuristics whale optimization algorithms Tuning Genetic algorithms

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Short-Term Load Forecasting Model Based on BWOA-LSSVM

Short-Term Load Forecasting Model Based on BWOA-LSSVM

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Kun Liu Bingbing Chen Juanjuan Wang Yuxin Yang Guangliang Sun Xiao Guo Weifang Power Supply Company State Grid Shandong Province Power Company Weifang China Key Laboratory of Intelligent Grid Ministry of Education Tianjin University Tianjin China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In recent years, advancements in new technologies and the emergence of new scenarios have caused a shift from the traditional pure load demand side to a broader load perspective, posing considerable challenges to conventional load forecasting methods. This transition creates an urgent requirement for power systems to develop regional load forecasting techniques tailored to new scenarios by leveraging contemporary theories and technologies. To enhance load forecasting accuracy, this paper introduces an improved whale optimization Algorithm (BWOA) based on the Baleen Search to optimize the Least Squares Support Vector Machine (LSSVM) model. The approach begins by establishing the LSSVM-based forecasting model. Subsequently, the whale optimization Algorithm (WOA) is refined by integrating adaptive weights and the Beetle Antennae Search (BAS) strategy. The BWOA is then employed to optimize the penalty factor of the LSSVM, thereby forming the BWOA-LSSVM model for load forecasting. Simulation comparisons demonstrate that the proposed BWOA-LSSVM model significantly improves the precision of power load predictions.

关键词： Adaptation models Accuracy Load forecasting Computational modeling Predictive models Prediction algorithms whale optimization algorithms Vectors Load modeling Convergence

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MCWOA based Hybrid Deep Learning for Detecting the Attacks in Cybersecurity with IoT Network

MCWOA based Hybrid Deep Learning for Detecting the Attacks i...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sravanthi Dontu Rohith Vallabhaneni Santosh Reddy Addula Piyush Kumar Pareek Haider Mohammed Abbas Department of Information Technology University of the Cumberlands Williamsburg USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

All throughout the globe, machine learning is being used in a diversity of settings, from universities to businesses. Intuitive machine learning algorithms can assess dangers and react quickly to security breaches and problems. In the realm of cybersecurity, it is essential for providing a proactive security system. Cybersecurity safeguards data, systems, and networks against unauthorized access in real time. Multiple analyses of privacy and security metrics over the past decade have found that the frequency and severity of cybersecurity breaches have been steadily increasing. Criminals are penetrating data protection systems at a rapid clip. Top cyber-security problems that need efficient explanations include anomaly finding, software vulnerability diagnosis, phishing page proof of identity, malware identification. In addition, NSL-KDD or KDD-CUP99 datasets provide the basis of most extant research. None of these datasets contain assaults that occurred recently. Networkflow ToN-IoT, a realistic dataset obtained from a large-scale, heterogeneous IoT network, was therefore utilized in the study. The study used a hybrid deep learning model combining ResNet and AlexNet for classification purposes. This study employs a hybrid optimization method that takes cues from both the Chimp optimization Algorithm (COA) and the whale optimization Algorithm (WOA) to fine-tune AlexNet's parameters. In order to recover the accuracy of MCWOA's identification, the Sobol arrangement is employed during the initialization of the population to distribute it uniformly over the solution space. Additionally, the position update process of classic MCWOA is enhanced by combining the bubble-net hunting and random search processes of the whale optimization algorithm. The results have important consequences for the continuous creation of adaptable, efficient, and secure intrusion detection systems for the complicated environment of IIoT networks Hybrid (ResNet-AlexNet) model reached the accuracy a

关键词： Deep learning Machine learning algorithms Accuracy optimization methods Intrusion detection whale optimization algorithms Classification algorithms Reliability Computer security Computer crime

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Research on Teaching Quality Evaluation Using Heuristic-Based Apriori Algorithm with Data Mining

Research on Teaching Quality Evaluation Using Heuristic-Base...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Fei Zhao Fan Yang Harbin Cambridge University Heilongjiang Harbin China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

An evaluation of teaching quality is a decision making of teaching value of teachers and learning of the students, becomes the significant role of teaching management and procedures in academics. Nevertheless, the operation of the executing the teaching quality evaluation is relatively unwieldy, frequently needed the accomplishment of the maximum number of data evaluation tasks. To overcome this problem, this research aims to proposes Heuristic approach for the effective teaching quality evaluation approach in the academics. Hence, this research proposes combined Firefly with whale optimization Algorithm based Apriori Algorithm (FWOA-AA) for the evaluation of the teaching quality. The data mining approach for the manual teaching is utilized and the FWOA-AA approach is utilized for attain the effective classification of the numerous students test score. The classification approach effectively chooses the classification features according to the rate of information gain. introduced FWOA-AA approach attains the effective classification accuracy of 95.01%, 97.39 % , 94.93%, 98.40% and 97.98% on the different data sizes like 100MB, 200 MB, 300 MB, 400 MB and 500 MB respectively.

关键词： Deep learning Accuracy Heuristic algorithms Education Focusing Manuals Network security whale optimization algorithms Classification algorithms Data mining

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Comparative Analysis of Cluster Specific Approach for Energy Management Using Meta-Heuristic Techniques

Comparative Analysis of Cluster Specific Approach for Energy...

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IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

作者： Gurjot Singh Vishnu Mangalam Mishra Abhi Bhatnagar Astitva Kumar Department of Electrical Engineering Netaji Subhas University of Technology Delhi India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

Demand-side management (DSM) has emerged as a pivotal approach in addressing the increasing global electricity demand amidst integrating smart grid technologies. DSM can reduce the peak load, enhance the grid reliability, and lower operational costs. This paper investigates the different optimization techniques, especially for residential customers, to minimize the total cost of electricity consumption and optimize consumption patterns. The following study considers two optimization techniques: whale optimization algorithm (WOA) and grey wolf optimization (GWO) and also investigates the impact of K-means clustering on the performance of the proposed scheme. Simulations are conducted on a realistic dataset of residential loads and compares the results of the proposed scheme with and without clustering. The simulation results show that GWO outperforms WOA regarding cost minimization and convergence speed. Furthermore, clustering can improve cost minimization and cause a significant dip from the peak cost.

关键词： Energy consumption Costs Demand side management Electricity Clustering algorithms Cost function Minimization whale optimization algorithms Sustainable development optimization

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Delay-Aware Probabilistic Cache Placement for Scalable Videos in Satellite-Terrestrial Networks

Delay-Aware Probabilistic Cache Placement for Scalable Video...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jiayi We Tianheng Xu Yuling Ouyang Jinfeng Tian Ting Zhou School of Microelectronics Shanghai University Shanghai China Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

With the rapid expansion of video services, caching has emerged as a promising solution to reduce retrieval delay and alleviate backhaul pressure. Specifically, caching video content on user devices and satellites is particularly effective for users without ground base station services. However, addressing the challenge of meeting users' varying video quality demands while reducing content transmission delay is critical. Therefore, this paper investigates a layered caching scheme based on Scalable Video Coding, which encodes videos into different layers to provide various quality levels and caches video content on different nodes. Furthermore, an optimization problem for the probabilistic cache placement scheme in a multi-layer caching model is proposed and solved using the Quasi-Oppositional whale optimization Algorithm. The simulation results indicate that, compared to other algorithms and benchmark schemes, the proposed scheme effectively reduces transmission delay.

关键词： Wireless communication Satellites Simulation Signal processing algorithms Static VAr compensators Probabilistic logic Space-air-ground integrated networks whale optimization algorithms Delays Video recording

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Energy-Efficient Prediction of Hourly Energy Consumption in Fog/Edge Computing Using Optimized Artificial Neural Networks

Energy-Efficient Prediction of Hourly Energy Consumption in ...

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Emerging Technologies (GCET), Global Congress on

作者： Hussam N. Fakhouri Mohannad Alkhalaileh Ahmad Hwaitat Eman Zaitoun Data Science and Artificial Intelligence Department College of Education Humanities and Social Sciences Al Ain University Al Ain United Arab Emirates

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

The rapid increase in global energy demand necessitates innovative strategies for efficient energy management, particularly in decentralized computing infrastructures such as fog and edge computing. These technologies reduce latency and bandwidth usage by bringing computation closer to the data source, but they introduce new energy management challenges. Accurate prediction of hourly energy consumption is crucial for optimizing these systems’ operation and sustainability. This study proposes a novel approach for predicting hourly energy consumption using a feed-forward multi-layer perceptron (MLP) artificial neural network (ANN) optimized by state-of-the-art metaheuristic algorithms. Historical energy consumption data, including computational workloads and environmental factors, train the ANN. Five metaheuristic algorithms—Marine Predator Algorithm (MPA), Four Vector Intelligent Metaheuristic (FVIM), whale optimization Algorithm (WOA), Equilibrium Optimizer (EO), and Self-Adaptive Differential Evolution (SaDE)—optimize the ANN’s weights. Results show that the Equilibrium Optimizer (EO) significantly enhances predictive accuracy, outperforming other algorithms. This optimized ANN model improves resource allocation, load balancing, and energy cost savings, contributing to the sustainability of fog and edge computing infrastructures. The findings highlight the importance of advanced predictive analytics in managing energy consumption within decentralized computing environments.

关键词： Energy consumption Accuracy Metaheuristics Artificial neural networks Prediction algorithms Energy efficiency whale optimization algorithms Vectors Sustainable development Edge computing

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Enhancing Data Quality in Smart Video Surveillance Systems: A whale Optimizer-Based Imputation Algorithm

Enhancing Data Quality in Smart Video Surveillance Systems: ...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Mahmoud M. Eid Abdelatif H. Abouali Kamal ElDahshan Department of Computer Science El-Shorouk Academy Cairo Egypt Faculty of Science Al-Azhar University Cairo Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

One of the most challenging aspects of data analysis, especially in video surveillance, is missing data. Missing data can lead to biased conclusions and reduced statistical power, thus affecting the accuracy and reliability of analyses. By introducing an optimization-oriented approach to data imputation, this study addresses the problem of missing data in tabular data, which is suitable for many applications. We propose a novel algorithm, the whale Optimizer-based Imputation Algorithm (WO-IA), which uses optimization techniques to select more effective features for imputation while ignoring less important features. This strategic feature selection improves the accuracy of the imputation results and increases the efficiency of t he computations even with high missing rates. To evaluate the performance of our proposed novel approach (WO-IA), we compare it with several state-of-the-art missing value imputation methods in different experimental scenarios on synthetic and video surveillance datasets with different missing value rates. The results show that WO-IA is robust and stable, making it a reliable choice for missing data imputation in different scenarios. These results demonstrate the effectiveness of the algorithm in improving the accuracy and reliability of video surveillance systems.

关键词： Accuracy Data analysis Data integrity Video surveillance Feature extraction Imputation whale optimization algorithms

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