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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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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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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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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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Optimizing channel cross-section based on cat swarm optimization

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WATER SCIENCE AND TECHNOLOGY-WATER SUPPLY 2016年第1期16卷 219-228页

作者： Liu, Dong Hu, Yuxiang Fu, Qiang Imran, Khan M. Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab Water Saving Agr Univ Heilongjiang Prov Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Heilongjiang Prov Collaborat Innovat Ctr Grain Pr Harbin 150030 Heilongjiang Peoples R China Northeast Agr Univ Key Lab High Efficient Utilizat Agr Water Resourc Minist Agr Harbin 150030 Heilongjiang Peoples R China Jilin Agr Sci & Technol Coll Changchun 132101 Jilin Peoples R China

Optimal design of channel cross-section is an important task in the hydraulic design of open channels. The traditional methods and models which neglect the frost heave are trial procedures and may result in failure of channels in design of irrigation channels. To improve the total cost, reliability and effectiveness, the model which is used in this study, is not only minimizing the cost of land acquisition but also the cost of concrete lining considering cost as the objective function. The constrained optimization model which considers values of thickness of channel concrete slab constraint simultaneously along with the objective of minimization of cost is propounded and solved using a recent global optimization technique, namely cat swarm optimization (CSO). The optimized channel section not only satisfies the optimal hydraulic cross-section but guarantees the safety and stability of the side walls so that both the amount of the concrete lining and the land acquisition are optimized. Finally, we take a main channel of Qinghe Irrigated Area of Farm 853 in Heilongjiang Province as a study area. The results obtained using the CSO approach are satisfaction and the method can be used for reliable design of artificial open channels. Furthermore, we compare the CSO algorithm with a genetic algorithm (GA) and the particle swarm optimization (PSO) to verify the effectiveness of the cat swarm algorithm in the channel section optimization.

关键词： cat swarm optimization algorithm cross-section frost heave open channel

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Optimizing least-significant-bit substitution using cat swarm optimization strategy

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INFORMATION SCIENCES 2012年 192卷 98-108页

作者： Wang, Zhi-Hui Chang, Chin-Chen Li, Ming-Chu Feng Chia Univ Dept Informat Engn & Comp Sci Taichung 401724 Taiwan Dalian Univ Technol Sch Software Dalian Liaoning Peoples R China

Embedding secret data into a cover image using simple least-significant-bit substitution can degrade the image quality dramatically, especially when a large number of bits are substituted. The exhaustive least-significant-bit substitution method is proposed to solve this problem. However, the idea has no practical application due to its long computation time. This paper adopts the cat swarm optimization (CSO) strategy to obtain the optimal or near optimal solution of the stego-image quality problem. The CSO strategy is generated by observing the behavior of cats, which has been proved to achieve better performance on finding the best global solutions. We revised the CSO strategy in our proposed scheme to make it practicable and suitable to solve the mentioned problem. The experimental results show that the proposed scheme can obtain a better solution with less computation time. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Image hiding Least-significant-bit substitution cat swarm optimization algorithm

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Enhancement of Power System Stability Employing cat swarm optimization based PSS

Enhancement of Power System Stability Employing Cat Swarm Op...

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International Conference on Electrical, Electronics, Signals, Communication and optimization (EESCO)

作者： Naresh, G. Raju, M. Ramalinga Narasimham, S. V. L. Pragati Engn Coll Elect & Elect Engn Surampalem Andhra Pradesh India Univ Coll Engn Kakinada Elect & Elect Engn Kakinada Andhra Pradesh India JNTUK Foreign Univ Relat Kakinada Andhra Pradesh India JNTUH Sch Informat Technol Comp Sci & Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479976782

Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie-lines. Power System Stabilizers (PSS) are incorporated in the excitation system of the generators to enhance damping of these low frequency oscillations. In this paper, the design of multiple PSS in a multi-machine power system (MMPS) using cat swarm optimization (CSO) algorithm has been proposed. The design problem of the PSS has been formulated as an optimization problem and CSO has been employed to search for optimal controller parameters. It has been shown that the stability performance of the system can be improved by minimizing the eigenvalue based objective function comprising of the damping factor and the damping ratio of the poorly damped electromechanical modes. Eigenvalue analysis and nonlinear simulation results have also been presented for various loading conditions and system configurations to show the effectiveness and robustness of the proposed controllers and their ability to provide efficient damping of low frequency oscillations.

关键词： Damping cat swarm optimization algorithm Power System Stabilizer Multi-machine Power System

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Ant Colony Based on cat swarm optimization and Application in Picking Robot Path Planning 7

Ant Colony Based on Cat Swarm Optimization and Application i...

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7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Xun, Yanqin Liu, Mingshan Zhou, Yuan Wang, Rui Zhang, Wenbo Jilin Univ Coll Commun Engn Changchun Jilin Province Peoples R China

ISBN: (纸本)9781467399043

In view of solving multi-objective path planning in the static environment, there are some faults for ant colony optimization(ACO), such as the long computation and easy to fall into local optimum. To solve these problems, the ACO based on cat swarm optimization (CSO) algorithm searching model (CSOACO) is presented. In this algorithm, the introduction of CSO algorithm search pattern realizes the local search in the current solution for ant colony individuals, which not only enrich the diversity of solution, but improve the accuracy of the algorithm. Finally, the new algorithm is simulated in MATLAB for picking robot multi-objective path planning problem. Through the simulation analysis, not only set the parameters, but compare CSOACO with other algorithms. Simulation results show that the algorithm can accelerate the convergence speed, search to the global optimal solution and realize the multi-objective path planning of picking robot.

关键词： ant colony optimization algorithm cat swarm optimization algorithm picking robot path planning

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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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