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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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Taxonomy of Diverse Soft Computing Techniques for Various Applications

Taxonomy of Diverse Soft Computing Techniques for Various Ap...

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Harminder Kaur Neeraj Raheja Amit Kumar Bindal Department of Computer Science and Engineering Maharishi Markandeshwar Engineering College Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Soft computing techniques have made significant contributions to modern life by solving a broad category of problems with effective answers. Even though optimization problems can be extremely complicated, they are typically dealt with and solved by combining the practitioner's many abilitiess, like instinctive behavior, prior knowledge, logical reasoning, mathematical elaboration, and physical sensation. When applied within the "soft computing" paradigm, heuristics theories and algorithms can offer a methodical and reasonable means of addressing optimization issues. In this work, we offer A comprehensive description of the most recent soft computing applications in a variety of fields. To address various societal issues and their existing solutions, a comprehensive overview of various methods for soft computing, such as neural networks, swarm intelligence, evolutionary computing, fuzzy computing, and their hybrid combinations, is presented.

关键词： Surveys Support vector machines Reviews Neural networks Taxonomy Sensitivity and specificity whale optimization algorithms

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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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Distributed Task Offloading in Mobile Edge Computing using Metaheuristics

Distributed Task Offloading in Mobile Edge Computing using M...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Ala’ A. Samarneh Abdallah Y. Alma'aitah Dept. of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Mobile Edge Computing (MEC) is emerging as a prominent technology that enables different sophisticated mobile applications. With the high transmission rates provided by 5G and beyond and the MEC technology, satisfying applications' strict delay requirements is possible. By providing high computation power closer to the users at the network edge, various applications such as smart cities, context awareness, and content distribution can provide users with a better Quality of Experience (QoE) using MEC. However, offloading all tasks to the MEC is inefficient, as transmitting tasks to the MEC drains the UE battery and might create congestion at the network edge. Therefore, the UE must determine the subset of tasks to offload to the MEC to minimize energy consumption and execution delays. In this work, we investigate a UE-driven offloading approach utilizing three metaheuristic algorithms: Hybrid Improved whale optimization Algorithm (HI-WOA), Tug of War optimization (TWO), and Adaptive Equilibrium optimization (AEO), and priority queues. We formulate the offloading decision as a constrained multi-objective optimization problem, where the objectives are to minimize the energy consumption and execution delay at the UEs. The HI-WOA, AEO, and TWO demonstrated robust performance through extensive simulations with an average Qualified Tasks Ratio (QTR) of 99.75%, 99%, and 94% respectively, and a significant reduction in the execution delays and energy consumptions under different workloads with varying requirements.

关键词： Energy consumption Multi-access edge computing 5G mobile communication Smart cities Metaheuristics Signal processing algorithms Signal processing whale optimization algorithms Delays Quality of experience

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Fault Diagnosis of Rolling Bearing Based on WOA-VMD-LSTM

Fault Diagnosis of Rolling Bearing Based on WOA-VMD-LSTM

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Song Xue Huazhan Gui Xinyu Qin Kang Xia Yihui Tao Jiaoyang Xi Northern Information Control Research Institute Group Co. Ltd Nanjing China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In order to solve the problems of difficulty in extracting fault features of rolling bearings and the impact of external excitation on the accuracy of fault diagnosis, this paper proposes a fault diagnosis method based on whale optimization algorithm and LSTM(Long Short Term Memory) improved variational mode decomposition combined with support vector machine optimized convolutional neural network. Firstly, the variational mode decomposition of the vibration signal of the rolling bearing is carried out, and the minimum envelope entropy is taken as the fitness function, and the whale optimization algorithm is used to determine the number of decomposition modes and the secondary penalty factor of variational mode decomposition. Secondly, the LSTM noise reduction network is constructed, and the modal components decomposed by the variational mode are introduced into the noise reduction network, and the vibration signal after noise reduction is obtained by reconstructing each mode after noise reduction. Finally, support vector machine is used as the classifier of the convolutional neural network, and the vibration signal after noise reduction is input into the convolutional neural network to realize fault diagnosis. The analysis of experimental results shows that the method proposed in this paper has high fault diagnosis accuracy and better robustness.

关键词： Fault diagnosis Vibrations Support vector machines Accuracy Noise reduction Rolling bearings Signal processing algorithms Feature extraction whale optimization algorithms Convolutional neural networks

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Cubic Chaotic Initialization Joint Orthogonal Crossbar Strategy to Improve the whale Algorithm

Cubic Chaotic Initialization Joint Orthogonal Crossbar Strat...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Ying Xia Qingsong Huo Yanchun Wang Yinbei He School of Communication and Electronic Engineering Qiqihar University Qiqihar China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

After the invention and popularization of computer and the continuous innovation and development of predecessors, swarm intelligence optimization algorithm theory has become an important tool in many fields. For example, whale algorithm is widely used in circuit automatic design, automatic control system and optimal design problems in aeronautical engineering. However, whale algorithm has some problems: the local search ability is weak, it is easy to fall into the local optimal solution, and the convergence speed is slow. This paper proposes an improved whale algorithm (COOLWOA): cubic chaotic mapping is used instead of random method to improve population diversity, and orthogonal opposition learning strategy is used to generate the design matrix of new solutions to ensure good coverage and diversity of new solutions in the whole search space. Then, the local optimization efficiency and global optimization performance of whales are enhanced by using vertical and horizontal strategy to improve the global search and utilization ability. Through MATLAB experiments, the results show that compared with the basic whale optimization algorithm, the proposed algorithm can improve the convergence speed and search ability.

关键词： Technological innovation Heuristic algorithms whales Space cooling Benchmark testing whale optimization algorithms Statistics

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Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Credit Rating Prediction Based on WOA-Cross-Validation-Light...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Zichang Sun Shandong University Weihai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

One of the key challenges faced by banks in the context of continuous growth in customer loan demand is how to effectively execute customer credit ratings. This study introduces SMOTE to solve the problem of data imbalance and utilizes RFE_XG-Boost for feature importance assessment to screen out the key features that have a significant impact on credit ratings. Based on these selected features, the LightGBM model is used to predict customer credit ratings. To further optimize the model performance, this paper applies the whale optimization Algorithm (WOA) to adjust the parameters of the LightGBM model and combines with the 5-fold cross-validation technique to enhance the model prediction accuracy. Using this method, the study significantly improves the accuracy of the model prediction. The experimental results confirm that the LightGBM model, which combines SMOTE, RFE_XGBoost feature selection and WOA, shows high accuracy and practicality in credit rating prediction. The methodology of this study provides an efficient solution for bank credit rating and lays the foundation for future research in similar areas.

关键词： Radio frequency Technological innovation Accuracy Predictive models Prediction algorithms Feature extraction whale optimization algorithms

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Intelligent Reflecting Surfaces Assisted UAV Reliable Communication

Intelligent Reflecting Surfaces Assisted UAV Reliable Commun...

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IEEE Conference on Wireless Communications and Networking

作者： Haiying Peng Yufeng Zheng Peng He Yaping Cui Ruyan Wang Dapeng Wu Luo Chen School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China

In this paper, we investigate the reliability of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications in the case of limited UAV energy. Under constraints of the UAV energy and the channel decoding error rate, we formulate a reliability maximization problem by jointly optimizing the IRS’s scheduling, the UAV’s trajectory, the IRS’s phase shift, and the UAV’s transmit power. Since the partial constraints of the problem are strictly nonconvex and its variables are coupling, the problem is difficult to convert to a nonconvex problem. Therefore, we propose a chaotic adaptation hybrid whale optimization algorithm (CAHWOA) to solve the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the binary optimization algorithm (BWOA). Simulation results demonstrate that the joint optimization of IRS and UAV can improve the system communication reliability by almost 32% compared with the two baseline schemes. CAHWOA can improve the convergence rate by nearly 20% and enhance the optimization-seeking accuracy by about 0.04 compared with the three baseline algorithms.

关键词： Couplings Chaotic communication Error analysis Simulation Autonomous aerial vehicles whale optimization algorithms Trajectory

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