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A hybrid modified grey wolf optimization-sine cosine algorithm-based power system stabilizer parameter tuning in a multimachine power system

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OPTIMAL CONTROL APPLICATIONS & METHODS 2020年第4期41卷 1143-1159页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India

Damping of low-frequency oscillations due to the unpredictable perturbations of a power network has always been a challenging task. In an interconnected power network, power system stabilizers (PSSs) are in practice to damp out these low-frequency oscillations by providing a necessary control signal to the automatic voltage regulator unit based on the deviation in generator speed/power output. This article proposes a novel approach of hybrid modified grey wolf optimization-sine cosine algorithm for tuning the parameters of PSS of an interconnected multimachine power system. The optimal parameter tuning of PSS with the proposed algorithm is achieved by considering a multiobjective function comprises of improving the damping and eigenvalue characteristics of the consolidated multimachine system. A benchmark model of two area four machine system is adopted to investigate the performance achieved with the proposed algorithm in the simultaneous damping of the local and interarea mode of oscillations in a multimachine power system. The system study has been carried out under a self-clearing fault condition, and the detailed analysis is presented by analyzing the eigenvalues, and their corresponding natural frequencies, damping ratios. The damping nature achieved for the system states under system uncertainties with the proposed algorithm is also presented. The performance obtained from the proposed hybrid algorithm has been compared with the standalone and state-of-the-art optimization methods.

关键词： hybrid optimization algorithm interarea oscillations low-frequency oscillation damping modified grey wolf optimization power system stabilizer sine-cosine algorithm

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A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails

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MATERIALS TESTING 2020年第3期62卷 251-260页

作者： Kurtulus, Dies Yildiz, Ali Riza Sait, Sadiq M. Bureerat, Sujin Kaen, Khon Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand

In this paper, a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO) and simulated annealing for the purpose of accelerating its global convergence performance and optimizing structural design problems. This paper is the first research study in which the hybrid Harris hawks simulated annealing algorithm (HHOSA) is used for the optimization of design parameters for highway guardrail systems. The HHOSA is evaluated using the well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a guardrail system that has an H1 containment level as a case study is optimized to investigate the performance of the HHOSA. The guardrail systems are designed with different cross-sections and distances between the posts. TB11 and TB42 crash analyses are performed according to EN 1317 standards. Twenty-five different designs are evaluated considering weight, the guardrail working width, and the acceleration severity index (ASI). As a result of this research, the optimum design of a guardrail is obtained, which has a minimum weight and acceleration severity index value (ASI). The results show that the HHOSA is a highly effective approach for optimizing real-world design problems.

关键词： Harris hawks algorithm Simulated annealing crash analysis hybrid optimization algorithm guardrails road safety barriers

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Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt Capacitors in Distorted Distribution Systems Using a hybrid Phasor Particle Swarm optimization and Gravitational Search algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第6-7期48卷 543-557页

作者： Milovanovic, Milos Tasic, Dragan Radosavljevic, Jordan Perovic, Bojan Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia Univ Nis Fac Elect Engn Nish Serbia

In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm optimization and Gravitational Search algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THDV) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results.

关键词： distribution system inverter-based distributed generation shunt capacitor optimal placement harmonic distortion hybrid optimization algorithm phasor particle swarm optimization gravitational search algorithm

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Certain investigation on optimization technique for sensor nodes in the bio medical recording system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 7145-7159页

作者： Ponsam, J. Godwin Nimala, K. Mohammad, Gousebaig Shitharth, S. Radha, Vijaya Kumar Reddy Rao, B. Srinivasa Srihari, K. Chandragandhi, S. SRM Inst Sci & Technol Dept Networking & Commun Kattankulathur India Vardhaman Coll Engn Hyderabad Comp Sci Dept Hyderabad India Kebri Dehar Univ Dept Comp Sci Kebri Dehar Ethiopia Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram AP India Lakireddy Bali Reddy Coll Engn Dept IT Mylavaram India SNS Coll Technol Dept Comp Sci & Engn Coimbatore India Karpagam Inst Technol AP AI & DS Coimbatore India

The creation of sensor-based software for health monitoring using Internet of Things (IoT) technology is the main goal of this project. The program's objective is to continuously monitor human physiological data, including ECG, SPO2, heart rate, and respiration, by employing biomedical sensor networks. These sensors collect data, which is then processed by a processor and transmitted to an edge server through a transceiver. A node of corner facilitates for real transmission has processed each data will be patient's phone and the clinicians' LED display. To address the optimization challenge, the program utilizes a Double Deep-Q-Network approach, with parameters optimized using a hybrid genetic algorithm-based simulated annealing technique. However, healthcare records obtained from the sensors are susceptible to change due to environmental factors, leading to potential performance issues. In order to overcome this challenge, an optimization approach is employed to refine the proposed technique, ensuring accurate prediction of readings. The study conducted experiments to evaluate the program's performance, utilizing various metrics and different parameters. The results to provide light on how well the program that was created for leveraging IoT technologies for health monitoring is working. This study presents an innovative sensor-based program for IoT technology-based health monitoring, which continuously monitors human physiological data. The program incorporates a hybrid optimization approach to ensure accurate prediction of readings, accounting for environmental factors. The proposed Double Deep-Q-Network and the evaluation metrics employed demonstrate the originality and contributions of this research in advancing health monitoring systems.

关键词： Biomedical record system double DQN bio-sensors edge computing hybrid optimization algorithm

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A hybrid snow ablation optimized multi-strategy particle swarm optimizer for parameter estimation of proton exchange membrane fuel cell

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IONICS 2025年第5期31卷 4535-4562页

作者： Aljaidi, Mohammad Agrawal, Sunilkumar P. Parmar, Anil Jangir, Pradeep Arpita Trivedi, Bhargavi Indrajit Gulothungan, G. Jangid, Reena Alkoradees, Ali Fayez Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Govt Engn Coll Dept Elect Engn Gandhinagar 382028 Gujarat India Shri KJ Polytech Dept Elect Engn Bharuch 392001 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Graph Era Hill Univ Dept CSE Dehra Dun 248002 Uttarakhand India Graphic Era Dept CSE Dehra Dun 248002 Uttarakhand India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Vishwakarma Govt Engn Coll Ahmadabad Gujarat India SRM Inst Sci & Technol Dept Elect & Commun Engn SRM Nagar Kattankulathur 603203 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 Tamil Nadu India Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan 320315 Taiwan

The research presents Snow Ablation Optimized Multi-strategy Particle Swarm optimization (SAO-MPSO) as an algorithm to perform accurate parameter estimation of proton exchange membrane fuel cells (PEMFCs). The four optimization methods PSO, PPSO, AGPSO, and VPPSO fail to achieve proper exploration-exploitation balance which results in poor parameter tuning outcomes. SAO-MPSO assumes a framework where snow ablation search elements combine with multi-strategy reproduction methods to accelerate both speed-to-convergence and analysis precision. SAO-MPSO demonstrates excellent accuracy and stability when tested on six commercial PEMFC models under different operating conditions. SAO-MPSO demonstrates superior performance by reaching the lowest error metrics alongside the fastest convergence speed thus becoming an optimal optimization tool for PEMFC modeling. The obtained results demonstrate the reliability of this method for fuel cell parameter optimization which can lead to its application in real-time energy systems. The upcoming research will concentrate on developing SAO-MPSO for extensive fuel cell implementations and additional energy technology domains.

关键词： PEMFC Parameter optimization hybrid optimization algorithm Snow Ablation Optimized (SAO) Particle Swarm optimization (PSO)

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Server deployment strategies considering robustness and resource constraints in edge computing

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2025年第1期14卷 1-24页

作者： Cao, Junjie Yu, Zhiyong Cao, Min Zhu, Baohong Yang, Jian Xian Res Inst High Technol Xian 710025 Peoples R China Natl Univ Def Technol Coll Informat & Commun Wuhan 430035 Peoples R China Tencent Cloud Comp Xian Co Ltd Xian 710075 Shaanxi Peoples R China

Edge computing, as an emerging computing paradigm, efficiently offloads computationally intensive tasks to edge servers, extending services from the cloud center to the edge and significantly improving the efficiency of network data processing. However, existing research mostly focuses on the ideal deployment and optimization of edge servers, such as minimizing latency and load balancing, with little attention given to strategies for handling abnormal situations like soft attacks or sudden failures. This study delves into how to formulate server deployment strategies aiming to maximize system robustness and minimize costs when edge servers face soft attacks or sudden failures. Drawing insights from graph theory and network connectivity, we propose a server deployment strategy that enhances system robustness under resource constraints. Specifically, we introduce the concept of edge-delay-tolerant networks, ensuring the rapid establishment of a complete "backup link" in case of service interruption by constructing backup relay nodes and routing information tables to safeguard system continuity and stability. Additionally, we innovatively design adaptive edge server deployment methods in "silent" and "active" modes to cater to varying demands in different fault scenarios. To validate the effectiveness of the proposed strategies, we construct a multi-objective optimization problem through mathematical modeling and devise an hybrid optimization algorithm for solving it. Experimental simulation results demonstrate that the algorithm can provide near-optimal performance, effectively enhancing system robustness under resource constraints. This research not only offers new insights and methods for fault response mechanisms in edge computing but also provides valuable references for practical server deployment strategies, thereby promoting further development of edge computing technology.

关键词： Edge computing Edge server deployment Robustness hybrid optimization algorithm

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A hybrid service selection optimization algorithm in internet of things

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2021年第1期2021卷 4-4页

作者： Zhang, Xiaofei Geng, Juncheng Ma, Jianwei Liu, Hao Niu, Shuangxia Mao, Wandeng State Grid Henan Elect Power Co Elect Power Res Inst Zhengzhou 450052 Peoples R China

With the scale of Internet of Things (IoT) continues to increase, it brings big challenges for service selection in a large-scale IoT. For solving this problem, a service selection method based on the enhanced genetic algorithm is proposed in this paper. To decrease the scale of service selection, this paper uses the lexicographic optimization approach and quality of service (QoS) constraint relaxation technique to find the candidate service with height QoS. Then, the IoT service selection problem is transformed into a single-objective optimization problem adopting a simple weighting method, and the final composite service meeting the user's QoS needs are obtained from the candidate service. The simulation results show that the proposed algorithm can efficiently and quickly achieve a composite service satisfying user's QoS needs, and is more suitable for solving the service composite problem in large-scale IoT services.

关键词： Internet of things hybrid optimization algorithm Service selection Quality of service

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Dynamic reactive power optimization method for active distribution network with new energy and electric vehicle charging stations connected to the grid 2

Dynamic reactive power optimization method for active distri...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Rao, Zhen Li, Rui Zhou, Shouhu He, Wei Yuan, Xuan Li, Jia State Grid Jiangxi Elect Power Res Inst Nanchang Jiangxi Peoples R China State Grid Jiangxi Elect Power Co Nanchang Jiangxi Peoples R China Chongqing Univ Technol Chongqing Peoples R China Nanchang Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798350349047;9798350349030

Taking into account new energy output, EV charging load and residential power load, a clustering-local relaxation-correction dynamic reactive power optimization spatio-temporal decoupling strategy is proposed. Firstly, K-medoids clustering algorithm is used to divide the time period. Then, different types of variables are optimized in the same period. Finally, the number of input groups of shunt capacitor banks (SCBS) in several periods is fixed, and the secondary static reactive power optimization is carried out by using continuous variables. According to the characteristics of the model, a hybrid optimization algorithm with cross-feedback mechanism is used to solve different types of variables, and an improved artificial hummingbird algorithm based on Tent chaos mapping and adaptive variation is proposed to improve the solving efficiency. The simulation results show that the proposed decoupling strategy can not only guarantee the dynamic constraints of discrete variables, but also obtain satisfactory optimization results, and the hybrid algorithm can effectively solve the nonlinear optimization problems of mixed integers.

关键词： Active distribution network new energy electric vehicles dynamic reactive power optimization k-medoids clustering hybrid optimization algorithm

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Numerical optimal control for nonlinear dynamical systems involving mixed-valued inputs and joint probability path-constraints

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NONLINEAR DYNAMICS 2025年第4期113卷 3453-3483页

作者： Wu, Xiang Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This paper proposes a numerical method for optimal control of nonlinear dynamical systems (OCNDSs) involving mixed-valued inputs (MVIs) and joint probability path-constraints (PPCs). By utilizing a transformation technology and a relaxation method, the original problem is first written as relaxation problem with only continuous-value inputs (CVIs). It is theoretically proved that the relaxation problem converges to the original problem. Then, a double-level approximation is proposed for the joint PPCs to obtain an approximate constrained optimal control problem (COCP) of the resulting relaxation problem and the convergence result of this approximation is established. Further, the resulting approximate problem is converted to a finite-dimensional problem (FDP) with bound constraints by using a control parametrization method, smoothing approximation, and penalty function. Following that, an improved sample approximation is proposed for transforming FDP into a deterministic problem while reducing computational costs. Finally, an improved greedy search strategy-based hybrid optimization algorithm (IGSSBHOA) is designed for OCNDSs based on the conjugate gradient method (CGM) and an improved greedy search strategy (IGSS), and its global convergence result is established. Anti-cancer drug scheduling (ACDS) is further generalized to demonstrate the validity of the proposed approach by considering the state variable/control input joint probability constraints. Simulation results illustrate that comparison against other typical approaches, the proposed approach not only has a better performance, but also is stable and robust.

关键词： Optimal control Nonlinear dynamical systems Mixed-valued inputs Joint probability path-constraints hybrid optimization algorithm

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Optimizing Resource Utilization in IoT based Smart Home Automation: A hybrid Crow Search and Border Collie optimization Approach

Optimizing Resource Utilization in IoT based Smart Home Auto...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Dhivya, V.P. Adhiyaman, S.V. Elangavi, S. Gokul, R. Department of Information Technology Tiruchengode637 215 India

ISBN: (纸本)9798350359688

Smart home automation systems are now essential parts of contemporary living, providing convenience, energy efficiency, and improved security. Efficient compute offloading solutions are crucial for achieving optimal performance and resource utilisation in these systems. This research presents a new method for improving the efficiency of resource usage in smart home automation. The method utilises a hybrid algorithm that combines the Crow Search algorithm (CSA) with the Border Collie Optimisation algorithm (BCOA). The hybrid method is designed to tackle the difficulties of computing offloading by combining the advantageous aspects of the collective foraging behaviour of crows and the intellect of border collies. We introduce the hybrid algorithm and describe its design, implementation, and assessment through simulation-based experiments. The results indicate that the proposed approach effectively optimises resource utilisation, enhances system efficiency, and improves overall performance in smart home automation scenarios. This research enhances the field of smart home automation by proposing a new optimisation technique specifically designed to meet the specific needs and limitations of these systems. © 2024 IEEE.

关键词： Border Collie optimization algorithm Computation Offloading Crow Search algorithm hybrid optimization algorithm Resource Utilization

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