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Application of Fuzzy Proportional Integral Derivative Controller in Automatic Generation Control Using Hybrid african vultures optimization algorithm-Pattern Search optimization algorithm for Frequency Control of Power System with Electric Vehicles

ELECTRICA

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ELECTRICA 2023年第3期23卷 414-428页

作者： Dash, Pabitra Mohan Baliarsingh, Asini Kumar Mohapatra, Sangram Keshori BEC Dept Elect Engn Bhubaneswar Odisha India GCE Dept Elect Engn Kalahandi Odisha India GCE Dept Elect Engn Keonjhar Odisha India

In the continually increasing size and configuration of the modern power system with unpredictable load and integration of electric vehicles (EVs), the usage of intelligent and effective scheme is essential to control system frequency. This work suggests a hybrid african vultures optimization algorithm (AVOA) and pattern search (hAVOA-PS)-based fuzzy proportional integral derivative (FPID) structure for frequency control of a nonlinear power system with EVs. To illustrate the dominance of the projected hAVOA-PS algorithm, initially, PI controllers are considered and results are equated with AVOA, particle swarm optimization, and genetic algorithm methods. To further enhance the dynamic performance, PID and FPID controllers are considered. The dominance of FPID over PID and PI controllers is shown. In the next step, EVs are integrated into the test system and a comparative investigation of hAVOA-PS-based PI/PID/FPID and FPID+EV is presented. To exhibit the superiority of projected frequency control scheme in maintaining the stability of system under different disturbance conditions like load increase in area-1 only, load decrease/increase in all areas and large load increase in all areas are considered. It is noticed that the proposed hAVOA-PS-based FPID controller in the presence of EV is able to maintain system stability for all the considered cases, whereas other compared approaches fail to maintain stability in some cases. The effectiveness of the projected frequency control method is equated with some recently suggested approaches in a standard two-area system. At last, MATLAB results are equated with Open Real-Time Digital Simulators (OPEN-RT) outcomes to validate the feasibility and effectiveness of the proposed scheme for practical applications

关键词： african vultures optimization algorithm automatic generation control fuzzy proportional integral derivative controller electric vehicles pattern search

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Scalable COVID-19 classification using map reduce framework and deep learning enabled hunter Jaya african vultures optimization

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Patle, Bhagyashree R. Vijayarajan, V. Vellore Inst Technol Sch Comp Sci & Engn SCOPE Vellore 632014 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

Coronavirus disease 2019 (COVID-19) is the infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which was first identified in patients with mild respiratory illness. Some patients having COVID-19 recover without special treatment, and some require severe medical attention. This COVID-19 medical data is of big data and it is significant to classify for intelligentization of medical information. In this paper, Hunter Jaya african vultures optimization-based Deep Belief Network (HJAVO_DBN) is proposed for big data classification. Initially, input data is subjected to Deep Fuzzy Clustering (DFC) for data partitioning, followed by a map-reduce framework that has a Mapper phase and a Reducer phase. In each mapper, preprocessing is done by Z-score normalization and feature fusion is done using the KumarHassebrook measure and Deep Residual Network (DRN). Afterwards, the output from the mapper phase is subjected to the reducer phase, where all fused data are initially merged and then fed to data augmentation for performing oversampling. Finally, big data classification is done by DBN that is structurally optimized using HJAVO, formed by the integration of Hunter Prey-Optimizer (HPO) and Jaya african vultures optimization (JAVO). Furthermore, JAVO is formed by combining the Jaya algorithm and african vultures optimization (AVO). Moreover, the proposed HJAVO_DBN is analyzed for its effectiveness depending on various measures, like accuracy, sensitivity, and specificity, with greater performance of 92.28 %, 91.60 %, and 92.99 %.

关键词： Hunter prey-optimizer Hunter Jaya african vultures optimization Deep fuzzy clustering Deep residual network african vultures optimization algorithm

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Inverse design of metamaterial for dual-band infrared camouflage along with dual-band thermal management

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OPTICS COMMUNICATIONS 2025年 574卷

作者： Fu, Zhichang Yang, Zhenning Wang, Fuqiang Cheng, Ziming Zhang, Ao-yu Meng, Weifeng Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China Harbin Inst Technol Weihai Sch New Energy Weihai 264209 Peoples R China

The optical metamaterial for infrared camouflage with thermal management is an artificial structure available to not only evade the detection of various thermal imagers operating in distinct infrared wavebands but also ensure the thermal stability of the target. However, designing such an optical metamaterial is challenging due to the necessity to optimize numerous structural parameters simultaneously, especially metamaterials that are compatible with multiple functions. Herein, this paper proposes a novel inverse design method, combining african vultures optimization algorithm (AVOA) with rigorous coupled-wave analysis (RCWA), to design an optical metamaterial capable of achieving infrared camouflage and thermal management compatibly. The proposed metamaterial enables dual-band infrared camouflage for mid-wave infrared (MWIR, 3-5 mu m) and long-wave infrared (LWIR, 8-14 mu m), along with thermal management through dual-band non-atmospheric windows of 5-8 mu m and 14-17 mu m. Comparing to other intelligent optimization algorithms, the AVOA is exceptionally efficient, which only takes less time than others to get better results. This work not only provides theoretical guidance to design an optical metamaterial for infrared camouflage with thermal management, but also has the potential to solve the multi-objective optimization problems of multi-band or wide-band radiative modulation.

关键词： Infrared camouflage Metamaterial african vultures optimization algorithm Inverse design

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Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Wang, Yuchao Tong, Kelin Fu, Chunhai Wang, Yuhang Li, Qiuhua Wang, Xingni He, Yunzhe Xu, Lijia Sichuan Agr Univ Coll Elect & Mech Engn Yaan 625099 Sichuan Peoples R China Sichuan Agr Univ Coll Econ Chengdu 611134 Sichuan Peoples R China

Traditional path planning algorithms still face significant challenges in large-scale scenarios with high-density irregular obstacles, such as low search efficiency, limited obstacle avoidance capabilities, and a tendency to get trapped in local optimum. To overcome these challenges, a hybrid route planning algorithm combining the Modified Golden Jackal optimization (MGJO) algorithm and the Improved Dynamic Window Approach (IDWA) is proposed. To resolve the issue of getting trapped in local optimum and enhance global search efficiency in global path planning, the MGJO algorithm is synthesized based on nonlinear energy attenuation, diverse search strategies, and a guiding mechanism inspired by african vultures. To improve obstacle avoidance efficiency and ensure smoother local paths, the IDWA algorithm is redesigned by optimizing the obstacle distance evaluation function. In global path planning, the MGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions. In three different environments, the average path length of the MGJO algorithm over the original algorithm is improved by 10.76%, 16.72% and 25.46%. In local path planning experiments for mobile robots, the IDWA algorithm avoids the local optimum in small and medium-sized maps. In large maps, it significantly reduces the number of the local optimum occurrences, from 6 times to 2 times. The feasibility of the algorithm is validated in real-world experiments.

关键词： Golden Jackal optimization algorithm african vultures optimization algorithm Dynamic Window Approach algorithm Mobile robot Path planning

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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

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Asian Journal of Civil Engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the african vultures optimization algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： african vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and african vultures optimization

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PROCESSES 2023年第4期11卷 1162页

作者： Liu, Qing Kosarirad, Houman Meisami, Sajad Alnowibet, Khalid A. Hoshyar, Azadeh Noori Chongqing Creat Vocat Coll Sch Artificial Intelligence Chongqing 402160 Peoples R China Univ Nebraska Lincoln Durham Sch Architectural Engn & Construct 122 NH Lincoln NE 68588 USA Illinois Inst Technol Dept Comp Sci Chicago IL 60616 USA King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia Federat Univ Australia Inst Innovat Sci & Sustainabil Brisbane Qld 4000 Australia

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and african vultures optimization algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly algorithm (FA), particle swarm optimization (PSO), and Harris Hawks optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

关键词： Aquila Optimizer african vultures optimization algorithm task scheduling fog computing cloud computing Internet of Things

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Optimal Placement of Solar Power Plant by Using AVOA algorithm

Optimal Placement of Solar Power Plant by Using AVOA Algorit...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Klington, A. George Karthikeyan, K. St. Mother Theresa Engineering College Department of Computer Science Engineering Tuticorin India St. Mother Theresa Engineering College Department of Electrical and Electronics Engineering Tuticorin India

ISBN: (纸本)9798331521394

Infrastructure development and its design are more complex because of its constraint on budget and efficiency. By remembering these difficulties, a modified electrical structure is necessary for the nation. The power system structure should not depend on the conventional system. This main criterion is being in our engineer's mind. But, strengthening loads and their necessaries do not accept them to sleep. To face complex structures and constraints-based infrastructure facilities, optimal places for renewable systems are necessary. Here, the optimal placement of solar power plant is taken as the primary concern and its related analysis is made. Even though many algorithms are coming from all over the world. The part of those algorithms is not analyzed in many environments. In this paper, an AVOA algorithm is used for our analysis. This part of our research makes us excited with its results. The IEEE 57 system is used for the analysis. © 2025 IEEE.

关键词： african vultures optimization algorithm CPF MATPOWER PSO PV cell voltage stability

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A hybrid approach in opinion leaders selection using african vultures optimization and hunger games search algorithms

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SOCIAL NETWORK ANALYSIS AND MINING 2024年第1期14卷 60-60页

作者： Aghdam, Samad Mohammad Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

Depending on the actions and reactions in social networks, the opinions of users may have an impact on each other. Nodes who can impact others' opinions are defined as opinion leaders. In this paper, we formulate Opinion Leader Selection (OLS) as an optimization problem, for this purpose, african vultures optimization algorithm (AVOA) and Hunger games search (HGS) algorithms are utilized to choose opinion leaders and optimize the results. The proposed method uses the topological characteristics of the network and the combination of relationships between users, also a model that includes four activities, stability, novelty, and popularity features to calculate the fitness function and other required parameters. The Social Network Analysis (SNA) parameters, SIR, and OSim models are used to investigate the proposed method. Based on the findings of the evaluation, the proposed method provides for the Bitcoin alpha dataset (0.88, 0.7, 0.97, 0.81) percent and the Bitcoin OTC dataset (0.76%, 0.64, 0.96, 0.77) percent for accuracy, recall, precision, and F1-score respectively. Also, on the SIR and OSim models analysis, compared to the other 8 approaches, the proposed method gives a better-spreading ability interval and results.

关键词： Opinion leader Network centrality Social networks african vultures optimization algorithm Hunger games search

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Dynamic Modeling and Controller Design for a Parabolic Trough Solar Collector

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IEEE ACCESS 2023年 11卷 33381-33392页

作者： Goel, Anubhav Mahadeva, Rajesh Patole, Shashikant P. P. Manik, Gaurav Indian Inst Technol Roorkee Dept Polymer & Proc Engn Roorkee 247667 India Khalifa Univ Sci & Technol Dept Phys Abu Dhabi U Arab Emirates

The current study unveils an analytical dynamic model of a parabolic trough solar collector (PTSC) and subsequently presents the process to derive the transfer function of the PTSC system. For any PTSC module, output temperature can be related to various operating parameters using the generalized transfer function derivation that has been presented here. Presented model and derivation are applied on a PTSC module tested by Sandia National Laboratory, USA and a transfer function relating the outlet temperature of heat transfer fluid (HTF) with flowrate is derived. PID controller is designed to maintain the HTF outlet temperature by manipulating the flowrate. Natural-inspired algorithms (NIAs), which are uncommon in the context of control systems for PTSCs, are used to tune controllers. Two NIAs, namely Self-adaptive Differential Evolution (SaDE) and african vultures optimization algorithm (AVOA), were used for tuning with minimization of integral of time-weighted absolute error (ITAE) as an objective. It is concluded that SaDE-PID tuned controller outperforms AVOA-PID tuned controller with lower ITAE and other controller characteristics. Also, the designed controller is examined on various grounds as transient response characteristics and performance indexes. This completes the investigation of the integrity of the presented model, process, and controller.

关键词： Analytical models Heating systems Solar energy Transfer functions Heat transfer Control systems Tuning african vultures optimization algorithm controller design dynamic modeling parabolic trough solar collector self-adaptive differential evolution

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A twofold hunting trip african vultures algorithm for the optimal extraction of photovoltaic generator model parameters

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第3期44卷 7001-7030页

作者： Belmadani, Hamza Kheldoun, Aissa Bradai, Rafik Mekhilef, Saad Siddique, Marif Daula Blida 1 Univ SET Lab Dept Elect Fac Technol Blida Algeria Univ Mhamed Bougara Inst Elect & Elect Engn Lab Signals & Syst Boumerdes 35000 Algeria Blida 1 Univ Dept Elect Fac Technol Blida Algeria Swinburne Univ Technol Sch Sci Comp & Engn Technol Hawthorn Vic Australia Virginia Tech Energy Harvesting & Mech Res Lab Dept Mech Engn Blacksburg VA USA

The development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the african vultures optimization algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models' accuracies were notably improved with RMSE scores of 6.9096 x 10(-4) and 7.4011 x 10(-4) respectively for the RTC France cell, and 1.4251 x 10(-2) for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices.

关键词： PV models parameter extraction metaheuristic optimization african vultures optimization algorithm root mean square error

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