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A new approach for service activation management in fog computing using cat swarm optimization algorithm

COMPUTING 2024年第11期106卷 3537-3572页

作者： Hashemi, Sayed Mohsen Sahafi, Amir Rahmani, Amir Masoud Bohlouli, Mahdi Islamic Azad Univ Qeshm Branch Dept Comp Engn Qeshm Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Inst Adv Studies Basic Sci Dept Comp Sci & Informat Technol Zanjan Iran Petanux GmbH Res & Innovat Dept Bonn Germany Inst Adv Studies Basic Sci IASBS Res Ctr Basic Sci & Modern Technol RBST Zanjan Iran

Today, with the increasing expansion of IoT devices and the growing number of user requests, processing their demands in computational environments has become increasingly *** large volume of user requests and the appropriate distribution of tasks among computational resources often result in disordered energy consumption and increased latency. The correct allocation of resources and reducing energy consumption in fog computing are still significant challenges in this field. Improving resource management methods can provide better services for users. In this article, with the aim of more efficient allocation of resources and service activation management, the metaheuristic algorithm CSO (cat swarm optimization) is used. User requests are received by a request evaluator, prioritized, and efficiently executed using the container live migration technique on fog resources. The container live migration technique leads to the migration of services and their better placement on fog resources, avoiding unnecessary activation of physical resources. The proposed method uses a resource manager to identify and classify available resources, aiming to determine the initial capacity of physical fog resources. The performance of the proposed method has been tested and evaluated using six metaheuristic algorithms, namely Particle swarm optimization (PSO), Ant Colony optimization, Grasshopper optimization algorithm, Genetic algorithm, Cuckoo optimization algorithm, and Gray Wolf optimization, within iFogSim. The proposed method has shown superior efficiency in energy consumption, execution time, latency, and network lifetime compared to other algorithms.

关键词： Service activation Energy consumption Container Fog computing cat swarm optimization algorithm

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Optimal Scheduling using Advanced cat swarm optimization algorithm to Improve Performance in Fog Computing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLIcatIONS 2023年第7期14卷 1059-1071页

作者： Huo, Xiaoyan Wang, Xuemei Jiaozuo Univ Informat Construct & Management Ctr Jiaozuo 454003 Henan Peoples R China Jiaozuo Tech Coll Acad Affairs Div Jiaozuo 454000 Henan Peoples R China

Fog computing can be considered a decentralized computing approach that essentially extends the capabilities offered by cloud computing to the periphery of the network. In addition, due to its proximity to the user, fog computing proves to be highly efficient in minimizing the volume of data that needs to be transmitted, reducing overall network traffic, and shortening the distance that data must travel. But this technology, like other new technologies, has challenges, and scheduling and optimal allocation of resources is one of the most important of these challenges. Accordingly, this research aims to propose an optimal solution for efficient scheduling within the fog computing environment through the application of the advanced cat swarm optimization algorithm. In this solution, the two main behaviors of cats are implemented in the form of seek and tracking states. Accordingly, processing nodes are periodically examined and categorized based on the number of available resources;servers with highly available resources are prioritized in the scheduling process for efficient scheduling. Subsequently, the congested servers, which may be experiencing various issues, are migrated to alternative servers with ample resources using the virtual machine live migration technique. Ultimately, the effectiveness of the proposed solution is assessed using the iFogSim simulator, demonstrating notable reductions in execution time and energy consumption. So, the proposed solution has led to a 20% reduction in execution time while also improving energy efficiency by more than 15% on average. This optimization represents a trade-off between improving performance and reducing resource consumption.

关键词： Scheduling fog computing optimal balancing cat swarm optimization algorithm

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Array Pattern optimization for Steerable Circular Isotropic Antenna Array Using cat swarm optimization algorithm

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WIRELESS PERSONAL COMMUNIcatIONS 2018年第3期99卷 1169-1194页

作者： Banerjee, Sudipta Mandal, Durbadal NIT Durgapur Dept ECE Durgapur India

In this paper the steerable isotropic circular array antenna is designed for reducing the side lobe level (SLL) using evolutionary optimization technique. The optimization techniques particle swarm optimization and cat swarm optimization algorithm are used to reduce the SLL as well as to steer the main beam in specific direction. In this design of steerable circular arrays the amplitude excitations are optimized. Obtained results show that the maximum peak of SLL of the resultant patterns are as per requirement. This paper present a good performance in the array factor response and suppressed SLL for different number of array elements with different steering angle of the main beam using evolutionary optimization technique.

关键词： Circular array antenna Isotropic element Array pattern Beam steering Side lobe level Evolutionary optimization Particle swarm optimization cat swarm optimization algorithm

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Well placement optimization using an analytical formula-based objective function and cat swarm optimization algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2017年 157卷 1054-1070页

作者： Chen, Hongwei Feng, Qihong Zhang, Xianmin Wang, Sen Zhou, Wensheng Geng, Yanhong China Univ Petr East China Sch Petr Engn Qingdao 266580 Shandong Peoples R China CNOOC Res Inst Beijing 100027 Peoples R China

Well placement optimization is a crucial and complex task in oil field development. Well placement is usually optimized by coupling reservoir numerical simulator with optimization algorithm. This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency. In this work, a well placement optimization method using an analytical formula-based objective function and cat swarm optimization (CSO) algorithm is established. The objective function, derived from fluid flow in porous media and material balance principle, can be calculated by the analytical formula to avoid running reservoir numerical simulator. Then the well placement optimization model is built and solved by CSO algorithm. Three examples are applied to justify the feasibility of the new objective function and the efficiency of this optimization method. Results demonstrate this method can significantly accelerate the speed of well placement optimization process. It can help to determine the optimal well placement more efficiently for actual oilfield development.

关键词： Well placement optimization efficiency Analytical formula Objective function cat swarm optimization algorithm

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CSSLnO: cat swarm Sea Lion optimization-based deep learning for fake news detection from social media

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4225-4241页

作者： Sirra, Kanthi Kiran Mogalla, Shashi Madhuri, Kandalam Basamma Department of CSE Gayatri Vidya Parishad College of Engineering (Autonomous) Andhra Pradesh Visakhapatnam 530048 India Department of CS & amp

Social media has effectively shortened the time for the distribution of information, which sometimes carry news when compared to traditional methods. The convenience and affordable instant access to data with revolution in mobile technology have directed the production of false news. Fake news has the potential to mobilize public opinion, causing social unrest. Therefore, it is essential to check the authenticity and credibility of news articles being shared on social media. Moreover, versions of fake news are very similar to the actual ones, so it is problematic for humans to recognize them. Therefore, Fake news identification from social media become an Artificial Intelligence (AI) problem. A cat swarm Sea Lion optimization (CSSLnO)-based Deep Quantum Neural Network (DQNN) is developed in this research for detecting fake news. The input is effectively acquired from different websites, such as Twitter and Facebook. Various unique features are extracted from FakeNewsNet and BuzzFeed-Webis Fake News Corpus for the effective fake news detection process. DQNN is applied for identifying fake news using the extracted features. The authors developed an optimization algorithm named CSSLnO and employed it to further train the DQNN model to increase the detection performance. The developed model outperformed the other existing fake news detection techniques with improved accuracy, sensitivity, specificity, and F-measure of 90.65, 91.90, 89.09, and 91.46%. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： cat swarm optimization algorithm Deep Quantum Neural Network Fake news detection Nature inspired algorithms Sea Lion optimization algorithm Sentiwordnet features

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Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range

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ENERGIES 2025年第7期18卷 1866-1866页

作者： Li, Da Liu, Lu Yue, Chuanxu Gao, Xiaojin Zhu, Yunhai Qilu Univ Technol Shandong Acad Sci Inst Automat Jinan 250000 Peoples R China Qilu Univ Technol Shandong Acad Sci Sci & Technol Serv Platform Jinan 250000 Peoples R China

The state of charge (SOC) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of SOC estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and SOC assessment using cat swarm optimization dual Kalman filtering (CSO-DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter filter for dynamic parameter identification, while the extended Kalman filtering (EKF) algorithm acts as a state filter for real-time SOC estimation. These two filters operate alternately throughout the iterative process. Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and SOC estimation. To support this algorithm, we establish an environmental temperature battery database and incorporate temperature variables to achieve accurate SOC estimation under variable-temperature conditions. The results indicate that creating a database that accommodates temperature variations and optimizing dual Kalman filtering through the cat swarm optimization algorithm significantly improves SOC estimation accuracy.

关键词： variable temperature environmental temperature battery database cat swarm optimization algorithm dual Kalman filtering SOC estimation

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Classification of brain tumours in MRI images using convolutional neural network through cat swarm optimization

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EXPERT SYSTEMS 2022年第9期39卷 e13021-e13021页

作者： Deepak, V. K. Sarath, R. Noorul Islam Ctr Higher Educ Dept Elect & Commun Thuckalay Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Elect & Instrumentat Thuckalay Tamil Nadu India

In humans, a brain abnormality is a serious illness. Cancer which is the greatest cause of mortality can develop from the tumour. Magnetic resonance imaging (MRI) is among a more extensively utilized medical imaging modalities in brain tumours, then it has become the primary diagnosing mechanism for the treatment and evaluation of brain tumours. Computer-assisted diagnosis has become a requirement due to the exponential expansion in the quantity of MRIs acquired because of these programmes. Computer-assisted diagnosis strategies created to increase detection without many systematized readings failed to produce significant improvements in performance measurements. In this regard, the usage of deep learning-based automatic image processing algorithms appears to be a viable route for identifying brain cancer. In this research, introduce a cat swarm optimization (CSO) algorithm based upon a convolutional neural network (CNN) model utilized to segmentation in a classification of brain tumour. Results of experiments on MRI images using the BRATS dataset show that the CSO algorithm-CNN model achieved high-performance in term of 98% of accuracy, precision, specificity, sensitivity and F-score in the proposed classification task when compared to other classification approaches like support vector machines (SVM) as well as back propagation neural networks (BPNN).

关键词： brain tumour cat swarm optimization algorithm convolutional neural network MRI image

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Channel Estimation in MIMO TFT-OFDM Using Hybrid BESOA- CSOA algorithms

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第1期31卷 151-155页

作者： Venkatramanan, M. Chinnadurai, M. EGS Pillay Engn Coll Nagapattinam India EGS Pillay Engn Coll Dept Comp Sci & Engn Nagapattinam India

In wireless communication systems, maximizing spectral efficiency and enhancing link reliability are key objectives. One effective solution is the combination of Orthogonal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output (MIMO) techniques. OFDM divides the frequency band into non-overlapping sub-bands, enabling parallel data transmission. This helps overcome the limitations of traditional wireless communication systems with high-rate input data streams. MIMO-OFDM has emerged as a promising technology for achieving high data rates and robustness in wireless communications by supporting multiple inputs and outputs. However, optimizing both spectral efficiency and performance in rapidly fading channels can be challenging. To address these issues, we propose Time Frequency Training Orthogonal Frequency Division Multiplexing (TFT-OFDM). This approach utilizes group pilots for spectrum efficiency and shared channel estimates to maintain system performance stability. Channel estimation plays a critical role in TFT-OFDM, and both Least Square (LS) and Minimum Mean Square Error (MMSE) methods are considered. LS estimation has a simple approach but lower performance, while MMSE significantly reduces mean square error at the cost of computational complexity. To estimate the channel in TFT-OFDM, joint time and frequency channel estimation techniques are employed. This involves using sophisticated algorithms to optimize parameters such as maximum or minimum function values in the solution space. The intelligent algorithms, specifically the Bald Eagle Search optimization (BESOA) technique and the cat swarm optimization algorithm (CSOA), are utilized to estimate the channel efficiently in spectrally efficient MIMO-OFDM wireless networks. Performance improvement is observed with the intelligent algorithms. For instance, at a signal-to-noise ratio (SNR) of 15 dB, the proposed BESOA method achieves a Bit Error Rate (BER) of 10-6, while TFT-OFDM with group pi

关键词： Bald Eagle Search optimization technique cat swarm optimization algorithm MIMO OFDM Time Frequency Training Orthogonal Frequency Division Multiplexing

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A Dynamic DOA Tracking Method for Coherent Signal Sources Based on cat swarm algorithm 15

A Dynamic DOA Tracking Method for Coherent Signal Sources Ba...

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15th IEEE International Conference on Signal Processing (ICSP)

作者： Li, Jinman Diao, Ming Chen, Zengmao Bai, Yongzhen Harbin Engn Univ Sch Informat & Commun Eengn Harbin Peoples R China

ISBN: (纸本)9781728144801

Aiming at the time-varying incident angle of the coherent signal source, this paper studies the effect of updating the data covariance matrix and the value of the forgetting factor on the tracking result. On the basis of this, the idea of group intelligent search using the cat swarm optimization (CSO) algorithm is used to calculate the maximum likelihood (ML) algorithm. The huge amount of problems is improved, and a cat swarm optimization-maximum likelihood (CSO-ML) algorithm dynamic DOA tracking method is proposed. This method does not need to pre-process the sample covariance matrix using decoherence technology, and can directly process the coherent signal source, and it is still effective in the case that the algorithm such as projection approximation subspace tracking (Past) algorithm with a small forgetting factor value fails. Simulation experiments show that this method has better tracking accuracy under the condition of low signal-to-noise ratio (SNR) and small snapshots and can directly deal with coherent signal sources.

关键词： dynamic DOA tracking coherent signal source maximum likelihood estimation cat swarm optimization algorithm

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A meta-optimized hybrid global and local algorithm for well placement optimization

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COMPUTERS & CHEMICAL ENGINEERING 2018年 117卷 209-220页

作者： Chen, Hongwei Feng, Qihong Zhang, Xianmin Wang, Sen Ma, Zhiyu Zhou, Wensheng Liu, Chen China Univ Petr East China Sch Petr Engn Qingdao 266580 Shandong Peoples R China CNOOC Res Inst Beijing 100027 Peoples R China

Well placement optimization is a complex and time-consuming task. An efficient and robust algorithm can improve the optimization efficiency. In this work, we propose a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization. By coupling cat swarm optimization (CSO) algorithm, Mesh Adaptive Direct Search (MADS) algorithm, and Particle swarm optimization (PSO) meta-optimization approach, O-CSMADS has global search ability and local search ability. We perform detailed comparisons of optimization performances between O-CSMADS, hybrid cat swarm mesh adaptive direct search (CSMADS) algorithm, CSO, and MADS in three different examples. Results show that O-CSMADS algorithm outperforms stand-alone CSO, MADS, and CSMADS. Besides, optimal controlling parameters are not same for different problems, which indicates that the optimization of algorithmic parameters is necessary. The proposed method also shows great potential for other petroleum engineering optimization problems, such as well type optimization and joint optimization of well placement and control. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Well placement optimization efficiency Hybrid optimization algorithm Meta-optimization approach cat swarm optimization algorithm Mesh Adaptive Direct Search

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