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12th International Conference on Smart Grid (ICSmartGrid)

作者： Ali, Atif Ahmed, Md Mushfiqur Zeesan, Ashif Al Nayem Apon, Hasan Jamil Abid, Md Shadman Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9798350361612;9798350361629

In electrical distribution networks, capacitor banks are widely used to regulate reactive power and enhance power factor. The efficient placement of capacitor banks offers numerous advantages, including minimizing power losses, increasing overall cost savings, and enhancing voltage profiles across feeders. Numerous optimization algorithms have been applied to determine the optimal size and allocation of capacitor banks within distribution networks. This study proposes a solution to the Optimal Capacitor Placement and Sizing (OCPS) problem using the african vulture optimization algorithm (AVOA) that mimics the natural foraging and navigational behavior of african vultures. Employing the AVOA algorithm identifies the most suitable buses for capacitor allocation and efficiently determines ideal locations and sizes for capacitor banks. The effectiveness of the proposed approach is validated on the IEEE-33 and IEEE-69 bus Radial Distribution Systems (RDS) and compared with other available optimization techniques. The results indicated that implementing AVOA leads to improved overall performance, leading to a reduction in total cost and loss, improved voltage profile, energy savings, and net savings in dollars per year. The annual net savings for IEEE-33 and IEEE-69 buses are 32.22% and 33.24%, respectively.

关键词： Shunt Capacitors Optimal Capacitor Placement IEEE-33 and IEEE-69 RDS african vulture optimization algorithm

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Consistent african vulture optimization algorithm for electrical energy exchange in commercial buildings

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ENERGY 2025年 318卷

作者： Yin, Linfei Tian, Jing Chen, Xiaofang Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Guangxi Peoples R China Guangxi Univ Sch Publ Policy & Management Nanning 530004 Guangxi Peoples R China

As the electricity market evolves and the penetration rate of distributed controllable resources in commercial buildings increases, the potential for commercial building electricity trading emerges. However, in regional commercial building electricity trading, problems such as little study and no information sharing exist. Deficiencies exist in the existing algorithms, such as the computational complexity of the african vulture optimization algorithm (AVOA), and the unreliability of the consistent optimization algorithm in optimization search. For this reason, this study proposes a consistent AVOA, which incorporates consistent optimization algorithm based on the AVOA. The proposed consistent AVOA can improve the accuracy of optimization, precisely optimize the electrical energy exchange of commercial buildings, and enhance the utilization efficiency of distributed power generation equipment. Variable load, variable photovoltaic power, variable ambient temperature, and extreme cases verify the feasibility and reliability of the consistent AVOA through simulation. Compared to the traditional algorithms multi-verse optimizer and sine cosine algorithm, the proposed consistent african vulture optimization algorithm has a higher average value of revenues for the power selling companies. The proposed algorithm offers favorable stability. The convergence time for the consistent african vulture optimization algorithm is 20.8 % shorter compared to both the comparison algorithms. Additionally, the proposed algorithm reduces the cost of running commercial buildings by 12.9 % and 7.2 %, respectively.

关键词： optimization problems Electrical energy exchange in commercial buildings african vulture optimization algorithm Consistent optimization algorithm Regional commercial buildings

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Trigonometric-based mechanisms hybridized african vulture optimization algorithm for multi-manned disassembly line balancing involving worker heterogeneity and collaboration

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年 1-57页

作者： Huang, Yufan Zhou, Binghai Tongji Univ Inst Ind Engn Sch Mech Engn Caoan Rd 4800Mech Bldg A444 Shanghai 201804 Peoples R China

The rapid replacement of large-scale end-of-life (EOL) heavy machineries like automobiles, aircrafts and industrial robots necessitates efficient resource recovery to promote sustainable and eco-friendly manufacturing. This study therefore focuses on multi-manned disassembly lines in recycling large-scale products, bridging the gap between theory and practice. We introduce complex, safety-sensitive tasks that require collaborative efforts of multiple workers in the Multi-Manned Disassembly Line Balancing Problem (MMDLBP) for the first time. We also consider worker heterogeneity due to varying training and skills, as manual stations are inherently worker-dependent in nature. To address this Multi-Manned Disassembly Line Balancing Problem with Worker Heterogeneity and Collaboration (MMDLBP-HC), we establish a mixed-integer programming model to minimize cycle time and labor cost simultaneously. Given its NP-hard nature, we develop a Multi-Mechanism-Enhanced Bi-Objective african vultures optimization algorithm (MBAVOA). It employs specified encoding with numerical branching, precedence-priority concurrent decoding, and selective opposition-based learning. We also combine trigonometric-based mechanisms with the african vulture optimization algorithm (AVOA) to enhance exploration. Additionally, adaptive neighborhood search mechanisms are tailored for inter-individual information exchange. Numerical experiments compare MBAVOA to four meta-heuristics and an exact algorithm. The results demonstrate the model accuracy and the effectiveness of the encoding and decoding mechanisms, while MBAVOA outperforms benchmark algorithms significantly. Finally, we offer managerial applications to guide practitioners in balancing plan formation and training program design.

关键词： Multi-manned disassembly line balancing Worker heterogeneity Worker collaboration african vulture optimization algorithm Trigonometric-based mechanisms

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Improvement of Signal Decomposition Variational Mode Decomposition Method Based on african vulture optimization algorithm

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TRAITEMENT DU SIGNAL 2025年第1期42卷 19-27页

作者： Xie, Jiaye Min, Apan Kong, Weibin Nanjing Inst Technol Sch Appl Technol Nanjing 211167 Peoples R China Yancheng Inst Technol Coll Informat Engn Yancheng 224051 Peoples R China

This study introduces an enhanced Variational Mode Decomposition (VMD) method for signal decomposition, based on the african vulture optimization algorithm (AVOA). Addressing the limitations of the traditional AVOA in function optimization, this paper initially presents an Enhanced Convergence (EC) strategy. By refining the core formulas of AVOA, the EC strategy enhances its applicability and convergence performance in complex function optimizations. Building on this foundation, the EC-AVOA algorithm is utilized to globally optimize key VMD parameters, including the number of modes and penalty factors, thereby improving the adaptability and accuracy of the decomposition algorithm. Experimental results demonstrate that this method can effectively extract physically meaningful modal components from complex signals and has shown superior performance across various engineering applications. This research not only significantly enhances the decomposition effectiveness of the VMD algorithm but also expands the application scope of the EC-AVOA, offering an innovative optimization strategy for the field of signal processing.

关键词： african vulture optimization algorithm (AVOA) signal decomposition enhanced convergence strategy optimization in signal processing

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Solving bi-objective economic-emission load dispatch of diesel-wind-solar microgrid using african vulture optimization algorithm

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HELIYON 2024年第3期10卷 e24993页

作者： Mishra, Shilpa Shaik, Abdul Gafoor Indian Inst Technol Dept Elect Engn Jodhpur 342030 India Indian Inst Technol Ctr Emerging Technol Sustainable Dev Ctr IoT & Applicat Dept Elect Engn Jodhpur 342030 India

Microgrid is a localised power generation infrastructure designed to provide continuous and reliable power supply to a small, specific region. The increasing concern towards environmental sustainability has resulted in the prioritisation of non-emitting Renewable Energy Sources (RESs) while optimal sizing of microgrid. Optimal sizing of generation units at minimum cost with minimum emission satisfying various practical constraints is a challenging bi-objective optimization problem of power system known as Economic-Emission Load Dispatch (EELD). Metaheuristic approaches are predominantly used to solve the EELD problem. This article explores the advanced metaheuristic methods to solve EELD problem and proposes application of african vulture optimization algorithm (AVOA) to subsequently address the EELD problem of a microgrid combining diesel, wind, and solar energy sources based on field data of a specific location in Jaisalmer, India. AVOA emulates the foraging and navigation patterns of vultures, incorporating effective exploration and exploitation characteristics. The effectiveness of AVOA is first validated using three standard test systems of 10, 6 (IEEE30-bus), and 40 units with/without transmission losses, prior applying it for microgrid. The obtained results are compared with several other popular optimization techniques to establish the efficacy of proposed method. Further, AVOA is employed to analyse the impact of individual RESs on microgrid's cost and emissions across three distinct generation scenarios. The viability score is employed to evaluate the efficacy of all techniques along with other significant performance indices. Statistical data tests such as ANOVA, Wilcoxon, and robustness are employed to assess the statistical confidence of the AVOA. Additionally, a multi-comparison post-hoc TukeyHSD test is introduced which proves the superiority of AVOA. Results establish AVOA as the most effective solution for addressing the EELD problem in microgrid (a

关键词： african vulture optimization algorithm Economic-emission load dispatch Microgrid Solar-PV energy Statistical data tests Wind energy

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Implementation of african vulture optimization algorithm based on deep learning for cybersecurity intrusion detection

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ALEXANDRIA ENGINEERING JOURNAL 2023年 79卷 105-115页

作者： Alsirhani, Amjad Alshahrani, Mohammed Mujib Hassan, Ahmed M. Taloba, Ahmed I. El-Aziz, Rasha M. Abd Samak, Ahmed H. Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka Saudi Arabia Univ Bisha Coll Comp & Informat Technol Bisha Saudi Arabia Future Univ Egypt Fac Engn & Technol New Cairo Egypt Jouf Univ Coll Sci & Arts Qurayyat Dept Comp Sci Sakakah Saudi Arabia Menoufia Univ Fac Sci Shibin Al Kawm 32511 Egypt

The smart grid is an innovation that employs two-way communications to give innovative services to end consumers. Due to the severe contradictions in this connection, this system may be the target of numerous cyberattacks. Intelligent grid networks can be protected by employing intrusion detection systems. IDS increases smart grid security by identifying malicious activity in the networks. However, current detection methods have several areas for improvement, including a high false alarm rate and low detection accuracy. The paper proposes an innovative intrusion detection strategy for intelligent grids combining DL-based and feature-based techniques. For this, the dataset is pre-processed, and pre-processing is done by utilizing min-max normalization. Then, features including mean, median, mode, standard deviation, information gain, mutual information, correlation coefficient, data percentiles, and autoregressive data are extracted. Next, african vulture optimization algorithm organizes feature selection. Finally, DBN-LSTM is utilized for categorization to identify normal and attack packets. The developed method attains higher performance when compared with other existing techniques. Hence, the outcomes demonstrate that the AVOA-DBN-LSTM technique has a reliable potential for cybersecurity intrusion detection.

关键词： Intrusion detection Cybersecurity Smart grid african vulture optimization algorithm Deep belief network Long short-term memory

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Economic Load Dispatch Using african vulture optimization algorithm with Consideration of Valve-Point Loading

Economic Load Dispatch Using African Vulture Optimization Al...

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International Conference on Advanced Systems and Emergent Technologies (ICASET)

作者： Reddy, Kumeshan Moloi, Katleho Nelson Mandela Univ Dept Elect Engn Gqeberha South Africa Durban Univ Technol Dept Elect Power Engn Durban South Africa

ISBN: (纸本)9798350384901;9798350384895

This work presents the utilization of african vulture optimization algorithm (AVOA) to solve the Economic Load Dispatch (ELD) problem. To simulate practical conditions, both transmission losses and the effect of valve-point loading were considered. The algorithm was applied to a 6-bus system, under a 600 MW and 850 MW loading condition. The results were compared to that of other optimization techniques, such as Particle Swarm optimization ( PSO), Bat algorithm (BA), and Whale optimization algorithm (WOA). Results were analyzed in terms of best result, worst result, average value, and standard deviation. Results for the average test for both the 600 MW and 850 MW tests indicated the capability of the AVOA to produce average superiorities of up to 53.98% and 45.26% respectively. The convergence curves of the various tests indicated the capability of the AVOA to produce a superior result in a short space of time and converge quickly.

关键词： african vulture optimization algorithm economic load dispatch valve-point loading

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Wavefront correction based on african vulture optimization algorithm 2

Wavefront correction based on African vulture optimization a...

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2nd Conference on Space, Atmosphere, Marine, and Environmental Optics (SAME)

作者： Li, Xianshuo Yang, Huizhen Tang, Lingzhe Jinling Inst Technol Nanjing 211169 Peoples R China

ISBN: (纸本)9781510680838;9781510680845

Recent advancements in wavefront sensorless Adaptive Optics (AO) systems have attracted significant scholarly attention, primarily attributed to their free of wavefront detection, streamlined architectures, and enhanced functional capabilities. The control algorithm determines the correction performance of the wavefront sensorless AO system. Among the existing algorithms, certain algorithms demonstrate robust global convergence properties despite their relatively slow convergence rates. Some algorithms exhibit limited global optimization capabilities but converge more rapidly. Others achieve rapid convergence but require extensive preprocessing. The african vulture optimization algorithm (AVOA) is introduced to control the wavefront sensorless AO system in this paper, which is inspired by the observation of the hunting and flight behaviors of african vulture populations. In the Matlab programming environment, an adaptive optics system based on AVOA is built, which mainly includes a 61-element deformable mirror (DM) element and an imaging sensor device. Based on this AO system model, we studied the correction performance, convergence speed, and local convergence behavior of the AVOA control algorithm. For comparison, the correction performance of the adaptive optics system based on particle swarm optimization (PSO) and genetic algorithm (GA) is provided, respectively. The results show that AVOA can significantly improve the convergence speed of the adaptive optics system while ensuring global convergence. These results provide a new method for the control of wavefront sensorless AO systems, which can be applied in fields such as free-space optical communication, biological microscopy imaging, retinal imaging in the human eye, and environmental monitoring.

关键词： Adaptive optical system african vulture optimization algorithm Deformable mirror Wavefront correction

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Levy Flight and Chaos Theory-Based african vulture optimization algorithm for Electric Vehicle Charging Scheduling 43

Levy Flight and Chaos Theory-Based African Vulture Optimizat...

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43rd Chinese Control Conference (CCC)

作者： Li, Shanbin Liu, Qinglong South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Due to the improvement of people's environmental awareness and the advancement of battery technology, the number of electric vehicles (EVs) is on the rise. However, due to the time-consuming charging process and uneven distribution of charging stations across regions, proper scheduling and route management of electric vehicle charging are crucial. This paper develops a topological scheduling model utilizing real-world road networks to address challenges related to travel distance and extended queue time. The model strategically allocates appropriate charging stations for EVs, aiming to minimize both travel distance and queue time, while balancing the load between charging stations. Therefore, in order to improve the efficiency of finding scheduling solutions and overcome the convergence problem of multi-objective optimization, Levy flight and Chaos theory-based african vulture optimization algorithm (LCAVOA) is adopted as the algorithm for executing optimized scheduling. LCAVOA employs Levy flight for exploration, while chaotic mapping ensures effective search space exploitation. Of particular note is the enhanced performance of LCAVOA, especially in scenarios involving multiple vehicles. This study contributes new insights and methods for the field of EV charging scheduling optimization.

关键词： Electric vehicle Charging schedule african vulture optimization algorithm Chaotic maps Levy flight

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3-Tank System Liquid Level Control Using Improved african vulture optimization algorithm 7

3-Tank System Liquid Level Control Using Improved African Vu...

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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Chen, Xiaobo Wu, Guifeng Qiao, Wenwei Fang, Jiwei Jiang, Muzhou Ding, Pantao Yangzhou Univ Sch Elect & Energy Power Engn Yangzhou Jiangsu Peoples R China Jiangsu Huaneng Cable Co Ltd Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9798350375794;9798350375800

The three-capacity tank level system is a typical nonlinear and time-delayed system, and the PCT-III experimental device is used as the research object to establish its mathematical model through the mechanism method and test method. The african vulture optimization algorithm(AVOA) is an intelligent algorithm that simulates the behavior of a flock of birds, which has good convergence, but the optimization process is easy to fall into the local optimal solution. For this reason, an improved african vulture optimization algorithm is proposed. The improved african vulture optimization algorithm-based PID controller, which is proposed to address the shortcomings of the conventional proportional-integral-derivative controller in handling time delay and nonlinear objects, is utilized in the level control system of a three-capacity *** results show that the PID controller based on the improved african vulture optimization algorithm has better control effect than the conventional PID controller.

关键词： Three-capacity tank level system african vulture optimization algorithm PID controller

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