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3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Samal, Padarbinda Mohapatra, Alivarani Samal, Sarita Patel, Ranjeeta KIT Deemed to be University School of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350304732

Power electronics devices like the distribution static compensator (DSTATCOM) have been developed as a result of the rapid rise in load growth, cost of power generation, and power loss. In order to solve these issues, it is also crucial that these devices are allocated and sized properly. For determining the best position and rating of the DSTATCOM in this regard, population-based metaheuristic algorithms are shown to be extremely effective. The zebra optimisation algorithm (ZOA), a well-known metaheuristic algorithm, is used in this study to determine the best position and rating for a DSTATCOM while minimising the total real power loss (TORPL) in a 69-bus distribution system. Additionally, different techniques are used to confirm the effectiveness of the established ZOA in order to achieve the highest attainable percentage reduction in TORPL value. © 2023 IEEE.

关键词： Distribution static compensator distribution system real power loss zebra optimization algorithm

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Design of modified long short-term memory-based zebra optimization algorithm for limiting the issue of SHEPWM in multi-level inverter

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Krithiga, G. Senthilkumar, S. Alharbi, Mohammed Mangaiyarkarasi, S. P. Parisutham Inst Technol & Sci Dept Elect & Elect Engn NH 67Ring Rd Thanjavur 613006 India EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 Tamil Nadu India King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia Univ Coll Engn Dept Elect & Elect Engn Panruti Campus Panruti 607106 Tamil Nadu India

This research work proposes unique MLSTM-ZOA to quickly measure SAs for an MLI. Within a certain MI range, the suggested method may calculate a greater count of SAs with various solutions. Here, the main objective lies in minimizing the THD with consideration of three parameters such as MI, number of pulses per quarter cycle, and duty cycle. Furthermore, by achieving a lower fitness value in less iteration, the suggested method has definitely outperformed other methods with respect to convergence behavior, according to the data. Lastly, analysis and performance associated with the experimental validation of SHE in multi-level inverter are also carried out. The proposed MLSTM-ZOA model in terms of THD is 56.25%, 81.58%, 53.33%, and 41.67% better than MPA, HHO, MGMPA, and SF-BOA respectively. Similarly, the proposed MLSTM-ZOA model with respect to HDP is 64.03%, 47.16%, 84.01%, and 27.62% advanced than MPA, HHO, MGMPA, and SF-BOA respectively.

关键词： Multi Level Inverter Selective Harmonics Elimination Pulse width modulation Modified long short-term memory zebra optimization algorithm

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Tuberculosis detection bars on VGG19 transfer learning and zebra optimization algorithm

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Le, Tianzhi Shi, Fanfeng Ge, Meng Dong, Ran Shan, Dan School of Electronic and Information Engineering Carbon Based Low Dimensional Semiconductor Materials and Device Engineering Research Center of Jiangsu Province Yangzhou Polytechnic Institute Yangzhou225127 China Jiangsu Aodu intelligent Technology Co Yangzhou225012 China

Tuberculosis (TB) remains a significant global health challenge, necessitating accurate and efficient diagnostic tools. This study introduces a novel approach combining VGG19, a deep convolutional neural network model, with a newly developed zebra optimization algorithm (ZOA) to enhance the accuracy of TB detection from chest X-ray images. The zebra optimization algorithm, inspired by the social behavior of zebras, was applied to optimize the hyperparameters of the VGG19 model, aiming to improve the model's generalizability and detection performance. Our method was evaluated using a well-defined metric system that included accuracy, sensitivity, and specificity. Results indicate that the combination of VGG19 and ZOA significantly outperforms traditional methods, achieving a high accuracy rate, which underscores the potential of hybrid approaches in TB image analysis. © 2024 Le et al.

关键词： Medical Image Analysis Tuberculosis Detection zebra optimization algorithm

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UAV Obstacle Avoidance Trajectory Planning Based on Improved zebra optimization algorithm

UAV Obstacle Avoidance Trajectory Planning Based on Improved...

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第43届中国控制会议

作者： Qing Liang Miao Niu Xinyuan Xu Yue Hu School of Electronic Engineering Xi'an University of Posts and Telecommunications

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An upgraded zebra optimization algorithm(GSZOA) is presented to solve the issues of slow convergence speed and easy fall into local optimum of zebra optimization algorithm(ZOA) in obstacle avoidance trajectory planning of Unmanned aerial vehicle(UAV).In order to solve the problem of slow convergence,chaotic mapping is invoked for ZOA to enhance the population diversity and accelerate the convergence *** golden sine algorithm is integrated to improve the zebra position update formula,which effectively coordinates the global search and local mining *** at the problem of easily falling into local optimization,a cycle mutation strategy is introduced to reduce the probability of this ***,the improved algorithm is compared with the other five algorithms on the classical benchmark function to verify the superiority of the GSZOA *** is also applied to UAV obstacle avoidance trajectory planning,and the simulation results show that show that the GSZOA algorithm has better optimization seeking ability and faster convergence.

关键词： Unmanned Aerial Vehicle zebra optimization algorithm Bernoulli Chaos Mapping Golden Sine Obstacle Avoidance Trajectory Planning

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optimization HW-GTAW process parameters in the joining of austenitic stainless steel with historic data design and zebra algorithm

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第4期19卷 2587-2605页

作者： Padmanaban, M. R. Anantha Baskar, N. Senthilkumar, N. Seeman, M. Saranathan Coll Engn Trichy 620012 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Chennai 602105 Tamil Nadu India Annamalai Univ Annamalainagar 608002 Tamil Nadu India

This research article presents the optimization of the hot wire gas-tungsten arc welding (HW-GTAW) process parameters for joining AISI super 304 H (SS304H) plates with experiments designed using historic data design methodology. HW-GTAW can overcome the low deposition efficiency of the conventional GTAW process. The current of hot wire, welding current, and wire feed rate are the input factors, and the weight of weld deposition (WWD) and bead width (BW) are the outputs measured for analysis. Micrographs of weldment show defect-free joints;a higher wire feed rate leads to the disturbance of the main arc, whereas a lower wire feed rate results in droplet transfer, interrupting the welding. The weld interface exhibits the presence of finer grains and partially deformed grains in the partially mixed zone (PMZ) and coarser grains in a heat-affected zone (HAZ). The presence of columnar dendrites leads to superior tensile properties compared to the base metal. optimization shows that the joint fabricated with a welding current of 150 A, hot wire current of 40 A, and a wire feed rate of 1500 mm/min exhibits a higher WWD and BW of 23.97 g and 13 mm, respectively. Wire feed rate revealed a higher influence on WWD, and welding current has a higher effect on the BW of SS304H joints. The optimized SS304 H joint exhibits a maximum strength in tension (598 MPa) and a mean hardness of 188 HV at the fusion zone (FZ) and HAZ. Nature-inspired metaheuristic zebra optimization algorithm also produces identical results from desirability analysis of historical data.

关键词： Hot wire GTAW SS304H Historic data design optimization Desirability analysis zebra optimization algorithm

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Enhancing grid stability through virtual inertia control in automatic generation control of a multi area system

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ENERGY REPORTS 2025年 13卷 2764-2789页

作者： Lalngaihawma, Samuel Das, Sumana Datta, Subir Ustun, Taha Selim Cali, Umit Mizoram Univ Dept Elect Engn Aizawl 796004 India MLR Inst Technol Dept Elect & Elect Engn Hyderabad 500043 Telangana India Fukushima Renewable Energy Inst AIST FREA Koriyama 9630298 Japan Norwegian Univ Sci & Technol Dept Elect Energy OS Bragstads Plass 2E N-7034 Trondheim Norway Univ York Sch Phys Engn Technol York YO10 5DD England

In this paper, a four-area interconnected multi area system with hybrid generating units is modelled for the study of automatic generation control (AGC). In systems where renewable energy are present, the inertia of the system decreases due to the absence of rotor or rotating force causing system instability. A virtual inertia control is introduced to the system to compensate the loss of inertia using the proposed novel controller named tilt double integral derivative with filter controller (TI-IDN). The proposed controller is tuned with zebra optimization algorithm to improve the system response like settling time (ST), peak overshoot (POS) and peak undershoot (PUS). The superiority of the proposed controller is observed and damping ratio is improved by 93.31 %, 89.97% and 31.03 % respectively by comparing with Integral, Tilt-Integral and Tilt integral derivative controller. Analysis reveals that VIC coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by performing sensitivity analysis during wide variations of system loading and system with communication Time delay.

关键词： Automatic generation control Virtual inertia control zebra optimization algorithm Multi area system Deregulation

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Machine-learning model of nonlinear vibration prediction for mining riser used into deep-sea hydrate exploitation based on CLR-ZOA-RF

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NONLINEAR DYNAMICS 2025年 1-22页

作者： Guo, Xiaoqiang Lv, Junlin Li, Yingwei Ren, Yuanzhe Hu, Ning Song, Yuxuan Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China

The ocean mining riser represents the core equipment utilized for the commercial exploitation of deep-sea natural gas hydrates. It is subjected to a multitude of extreme environmental conditions, including ocean vortex excitation, the presence of large aspect ratios of structures, and the occurrence of internal multi-phase flow. These factors contribute to the generation of an exceptionally complex vibration mechanism. A machine learning model for predicting the nonlinear vibration of deep-sea hydrate mining riser is proposed by combining the zebra optimization algorithm (ZOA) with the Random Forest (RF). The model utilizes chaos mapping, levy flight, and the random perturbation mutation strategies (CLR) to improve the performance of the ZOA and achieve real-time prediction of the vibration response of mining riser under multiple factors. The model effectively solves the problem of traditional mechanical modeling and numerical solution accuracy being greatly affected by complex external factors. Using the principle of similarity, a simulation experimental device for deep-sea hydrate mining riser vibration is developed, and multi factor vibration experiments are conducted to construct an experimental dataset. The proposed CLR-ZOA-RF machine learning model is used to predict the root mean square (RMS) displacement of in-line (IL) and cross flow (CF) direction vibration of the mining riser under multiple factors. The predicted results are compared with experimental test data, and the determination coefficient of the ZOA-RF model for IL direction vibration is 0.9359, and which for CF direction vibration is 0.9264. The CLR-ZOA-RF model has a determination coefficient of 0.9719 for IL direction vibration and 0.9667 for CF direction vibration. Compared with the ZOA-RF model, the CLR-ZOA-RF model has reduced the mean absolute error (MAE) and root mean square error (RMSE) of IL direction vibration prediction by 26.83% and 33.72%, respectively, and which of CF direction vibra

关键词： Deep sea natural gas hydrates Mining riser Vibration prediction Random forest zebra optimization algorithm

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Implementing a radial basis function model to anticipate the outcomes of the gasification

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CHEMICAL PRODUCT AND PROCESS MODELING 2025年第1期20卷 57-78页

作者： Cui, Hongfang Su, Ning Cui, Hongyan Hebei Armed Police Corps Shijiazhuang 050000 Peoples R China Hengshui Univ Sch Math & Comp Sci Hengshui 053000 Peoples R China Hegang Digital Technol Co Ltd Shijiazhuang 050000 Peoples R China

Hydrogen (H-2) and nitrogen (N-2) are both critical components in gasification processes, making efficient conversion of carbonaceous feedstocks into valuable gases with reduced environmental impact indispensable. This study demonstrates a state-of-the-art approach to predictive modeling for these quantities at high accuracy while allowing cost-effective solutions under a variety of operational conditions, enhancing safety, and enabling data-driven optimization. This work develops a new framework that incorporates the radial basis function model with two state-of-the-art optimization algorithms, namely the zebra optimization algorithm (ZOA) and Flow Direction algorithm (FDA), to enhance the predictive accuracy of gasification processes. This is a new frontier in optimizing the sustainable conversion of carbonaceous feedstocks, demonstrating the potential of data-driven methodologies in process efficiency and environmental sustainability. The RBFD model resulted in outstanding anticipation capability for H2, reaching an exceptional R2 value of 0.997 during the whole period of testing. On the other hand, the RBZO framework proved to be the strongest predictor for N2 anticipation, presenting an outstanding R2 of 0.994 during the testing and validation phases. The RBFD and RBZO frameworks showed significantly higher productivity compared to the conventional RBF model, as evidenced by accuracy metrics like MSE, RMSE, and WAPE.

关键词： hydrogen nitrogen machine learning radial basis function zebra optimization algorithm Flow Direction algorithm

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Early Parkinson's disease diagnosis using Transition Propagation Graph Neutral Network with Dynamic Hunting Leadership optimization

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 101卷

作者： Subasree, S. Priya, S. Brinda, S. Sakthivel, N. K. Nehru Inst Engn & Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India St Peters Inst Higher Educ & Res Dept Comp Sci & Applicat Chennai Tamil Nadu India

Parkinson's disease (PD) is a neurological disorder characterized by tremors, rigidity, and impaired balance due to the degeneration of neurons. This paper proposes an Early Parkinson's disease Diagnosis using Transition Propagation Graph Neutral Network with Dynamic Hunting Leadership optimization algorithm using voice features/attributes (EPDD-TPGNN-DHLO) approach. First, the pre-processed voice data are analyzed using Synchrosqueezing Fractional Wavelet Transform (SFWT) to extract hand crafted features. Then, Dynamic Hunting Leadership optimization (DHLO) algorithm is employed to feature selection for identifying the most relevant features. The zebra optimization algorithm is employed to enhance the classification accuracy and optimize the weight parameters of Transition Propagation Graph Neural Networks (TPGNN). The proposed EPDD-TPGNN-DHLO method achieves 24.68% to 26.22% higher accuracy, 26.18% to 29.18% greater specificity, and 24.48% to 28.49% improved precision compared to the existing DPD-CNN-LSTM, PP-PD-ANN and EPDITSVS-ML models. Finally, the EPDD-TPGNN-DHLO approach demonstrates a significant improvement over the existing techniques.

关键词： Dynamic Hunting Leadership optimization Multimedia Graph Collaborative Filter Synchrosqueezing Fractional Wavelet Transform Transition Propagation Graph Neural Network Parkinson's disease zebra optimization algorithm

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A Multi-Stage optimization Approach for Satellite Orbit Pursuit-Evasion Games Based on a Coevolutionary Mechanism

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REMOTE SENSING 2025年第8期17卷 1441-1441页

作者： Wu, Jian Xu, Xusheng Yuan, Qiufan Han, Haodong Zhou, Daming Rocket Force Univ Engn Xian 710025 Peoples R China Aerosp Syst Engn Shanghai Shanghai 201109 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

For the satellite orbit pursuit-evasion game problem, this paper proposes a multi-stage optimization-based solution aimed at improving the confrontation strategies between task satellites and target satellites in complex space environments. The approach divides the satellite pursuit-evasion game into two phases: the "approach phase" and the "sustained phase". It dynamically optimizes the trajectories and strategies of the task and target satellites to achieve adaptive orbit control and behavior optimization. To enhance the global search capability and local convergence of the algorithm, this paper employs the zebra optimization algorithm, introducing a multi-population cooperative evolution mechanism, and integrates differential game theory to improve the stability and reliability of the game strategies. Simulation results demonstrate that the proposed method effectively enhances task efficiency under multiple constraints, dynamically adjusts the strategies of both the pursuer and the evader, and provides an efficient, scalable solution applicable to satellite pursuit-evasion games in complex space environments.

关键词： zebra optimization algorithm cooperative evolution satellite pursuit-evasion game optimization differential game theory

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