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Integrated planning of emission reduction based unit commitment and generation dispatch solution for sustainable power system

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Apon, Hasan Jamil Ahmed, Ashik Adib, Asif Ur Rahman Ahshan, Razzaqul Islamic Univ Technol Dept Elect & Elect Engn Gazipur 1704 Bangladesh Sultan Qaboos Univ Coll Engn Dept Elect & Comp Engn Muscat 123 Oman

The dynamic nature of consumer load demands requires power systems to make complex decisions about the operation of multiple generating units. The Unit Commitment Problem (UCP) optimizes unit scheduling over a 24-48 h horizon while considering operational constraints. Simultaneously, reducing greenhouse gas (GHG) emissions has become critical, making the Combined Economic Emission Dispatch (CEED) problem vital for balancing cost and environmental sustainability. This paper presents the african vulture optimization algorithm (AVOA) for solving UCP and the Multi-Objective african vulture optimization algorithm (MOAVOA) for solving the CEED problem. Using the IEEE 39-bus system with 10 generating units as a test case, AVOA achieves lower operational costs and faster convergence compared to existing UCP methods. MOAVOA balances fuel costs and emissions, reducing daily emissions by 3,882.76 tons and saving $176,119.03 in emission taxes, offsetting the slight fuel cost increase. Comparisons with MOPSO, MOABC, and MOAOA validate MOAVOA's superiority in minimizing costs and emissions. To evaluate the sensitivity of the proposed algorithm, the IEEE 118-bus system with 54 generating units was introduced. Furthermore, the results confirm the robustness and adaptability of AVOA and MOAVOA under varying load demands and conditions, demonstrating their effectiveness.

关键词： Unit commitment Economic Load Dispatch Combined Economic Emission Dispatch african vulture optimization algorithm Multi-Objective african vulture optimization algorithm

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A Prospective Technique for Damage Detection in Truss Structures Using the Fusion of DNN with AVOA

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第7期28卷 2920-2933页

作者： Nguyen, Quyet Huu Le, Thang Xuan Nguyen, Dang Le Minh Bui, Thanh Tien Nguyen, Nhung Cam Tran, Hoa Ngoc Univ Transportat & Commun Ltd Co UCT DX Lab Hanoi 100000 Vietnam Univ Transport & Commun Fac Civil Engn Dept Bridge & Tunnel Engn Hanoi 100000 Vietnam

In recent decades, the integration of optimization methods and Machine Learning (ML) models has garnered significant attention within the research community. In the pursuit of establishing a symbiotic relationship between ML and optimization algorithms, this study focuses on the fusion of the african vulture optimization algorithm (AVOA) - an optimization algorithm inspired by the foraging behavior of african vultures - with Deep Neural Networks (DNNs) - a prevalent model in ML for damage detection of a real large-scale bridge. In this research, AVOA, possessing a vast search space and the ability to autonomously adjust crucial parameters during the search process, such as flight velocity and learning rate, is employed to select informative features such as weight and biases of DNNs. This synergy allows the network to automatically adjust its potential. The technique is applied to a truss bridge, utilizing Finite Element Model (FEM) data that has been validated by real-world vibration measurements, resulting in precise damage identification even in the presence of white Gaussian noise. Evaluation criteria demonstrate enhanced accuracy and computational efficiency compared to the traditional neural network approach.

关键词： Structural health monitoring Damage detection african vulture optimization algorithm Deep neural network

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Impact of solid oxide FC-RFB and IPFC on a renewable multi-area power system using combined PI and FOPD controllers optimized by the african vulture algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-28页

作者： Saha, Arindita Bhaskar, Mahajan Sagar Elmorshedy, Mahmoud F. Almakhles, Dhafer J. Padmanaban, Sanjeevikumar Regent Educ & Res Fdn Grp Inst Dept Elect Engn Kolkata 700121 West Bengal India Prince Sultan Univ Coll Engn Renewable Energy Lab Riyadh Saudi Arabia Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta 31733 Egypt Univ South Eastern Norway Dept Elect Engn IT & Cybernet Porsgrunn Norway

The expanding complexity of modern energy systems and the increasing integration of renewable sources make stable load frequency control (LFC) in interconnected power networks a continuing issue. Traditional controllers, such as proportional-integral (PI), proportional-integral-derivative (PID), and other subordinate control methods, frequently fail to control frequency adequately, especially in multi-source generating systems. Furthermore, standard optimization techniques may exhibit sluggish convergence and inefficient tuning, limiting their usefulness in real-time applications. To address these problems, this study suggest an enhanced LFC framework for a three-area power system that includes thermal-biodiesel (Area-1), thermal (Area-2), and hydro-thermal (Area-3) components. The african vulture optimization algorithm (AVOA) is used to improve a novel PI(FOPD) controller that combines integer-order PI with fractional-order Proportional Derivative (FOPD). According to a comparative investigation, the AVOA-augmented PI(FOPD) controller outperforms conventional I, PI, and PID controllers in terms of transient responsiveness, stability, and convergence. Additionally, AVOA outperforms optimization approaches such as Cuckoo Search, Particle Swarm optimization, and the Firefly algorithm. The integration of a Dish-Stirling solar thermal system, a Flexible AC Transmission System (FACTS) device, and an energy storage component improves system robustness. The results show that the AVOA-optimized PI(FOPD) controller greatly enhances LFC performance, making it a promising alternative for current power networks.

关键词： Automatic generation control Biodiesel Interline power flow controller Redox flow battery Solid oxide fuel cell african vulture optimization algorithm Cascaded controller

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Parameter optimization in wire electrical discharge machining using bio-inspired algorithms and response surface methodology

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第2期19卷 705-732页

作者： Mohanraj, T. Thenarasu, M. Ragaventhra, B. Shree Pavilan, P. Jaswant, S. Kumar, R. Sandeep Panchu, K. Padmanabhan Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore India Anna Univ Coll Engn Guindy Dept Ind Engn Chennai India

Wire electrical discharge machining (WEDM) is a highly regarded machining approach for its exceptional accuracy and adaptability, there is a need to further enhance its performance by optimizing critical process parameters. The objective is to examine the optimized setting of WEDM parameters with a focus on improving key performance metrics such as material removal rate (MRR), surface roughness (SR), and taper angle (TA). The desired responses are greatly influenced by critical process parameters, including pulse ON Time (50-60 mu s), pulse OFF Time (25-34 mu s), gap voltage (25-250 V), peak current (1-6 A), and dielectric flow rate (1-3 LPM). This study investigates the use of novel bio-inspired optimization algorithms, namely the african vulture optimization algorithm (AVOA), Firefly algorithm (FA), and cuckoo search algorithm (CSA), for optimizing process parameters in WEDM. The results are compared with the RSM approach. It was found that a significant reduction in MRR of 57.97%, an increase in TA of 6.21%, followed by enhancement in the surface finish of 22.98% attained with AVOA. The optimal parameters obtained with AVOA are TON of 51 mu s, TOFF of 30 mu s, peak current of 1 A, gap voltage of 116 V, and dielectric flow rate of 3 LPM. The integration of the RSM with bio-inspired algorithms in the manufacturing domain can be implemented in the manufacturing industries.

关键词： WEDM parameters african vulture optimization algorithm Firefly algorithm Cuckoo search algorithm Bio-inspired algorithm RSM

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Predicting The Compressive Strength Of High-Performance Concrete Utilizing Radial Basis Function Model Integrating With Metaheuristic algorithms

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第8期28卷 1703-1715页

作者： Hu, Liwei Hunan Commun Polytech Changsha 410132 Peoples R China

Ordinary concrete is well-documented in the construction of ordinary buildings, but this type of concrete cannot be used for special structures such as dams, silos, and skyscrapers, due to low compressive strength (CS), durability, and workability. The solution to this problem is to use high-performance concrete (HPC). To improve the mechanical properties has been added some additives, such as water-cement ratio, fly ash, and blast furnace slag. However, achieving a suitable mix design of HPC is complex, time, and energy-consuming. For this reason, the usage of machine learning (ML) makes it easier to obtain the acceptable mix design saving time and money. The artificial neural network (ANN) model is the subset of ML, which the experimental tasks can replace. One of these neural networks is the radial basis function (RBF), with one input layer, one or more hidden layers, and one output layer. In addition, RBF is combined with the Sine Cosine algorithm (SCA) and the african vulture optimization algorithm (AVOA) to obtain the desired results close to the experimental values. At the end of this article, it is seen that the SCA algorithm can combined better with the RBF model and achieve favorable and more satisfactory results with more accuracy and fewer errors.

关键词： Compressive Strength High-performance concrete Radial Basis Function Sine Cosine algorithm african vulture optimization algorithm

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Using radial basis function for estimating the high-performance concrete compressive strength with optimizing AVOA and SSA

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 5695-5707页

作者： Zhang, Yongcun Bai, Zhe Henan Univ Urban Construct Sch Civil & Transportat Engn Pingdingshan 467036 Henan Peoples R China

Compressive strength (CS) is concrete's most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the african vulture optimization (AVOA) and Salp Swarm algorithm (SSA) techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R-2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty.

关键词： High-performance concrete compressive strength african vulture optimization algorithm salp swarm algorithm radial basis function

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Prediction of compressive strength of concrete for high-performance concrete using two combined models, SVR-AVOA and SVR-SSA

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 961-974页

作者： Ding, Baorong Wang, Qiong Ma, Yue Shi, Hongbin Harbin Univ Heilongjiang Key Lab Underground Engn Technol Harbin 150000 Heilongjiang Peoples R China Harbin Univ Sch Civil Engn Harbin 150000 Heilongjiang Peoples R China Heilongjiang Inst Technol Coll Civil & Architectural Engn Harbin 150000 Heilongjiang Peoples R China

High-performance concrete (HPC) is a crucial material for constructing critical structures, such as dams, bridges, and high-rise buildings, as its exceptional compressive strength (CS) is vital for ensuring structural integrity. To improve this strength, additives, such as fly ash (FA) and micro-silica (MS), can be added to the mixture, often by reducing the water-to-cement ratio, alongside other factors. However, accurate modeling is imperative to estimate the CS of HPC. In this paper, support vector regression (SVR) is a newly developed regression model demonstrating superior performance in HPC compressive strength. Furthermore, two novel optimization algorithms, the african vulture optimization algorithm (AVOA), and Salp Swarm algorithm (SSA), are utilized to improve the performance of the SVR model. Each SVR-AVOA and SVR-SSA hybrid model was evaluated by 168 experimental samples, of which 70% of the sample belonged to training and 30$ to testing sections. Results indicate that the coupled model, SVR-AVOA, with suitable values, containing R-2 = 0.973, RMSE = 2.79, MSE = 7.82, NRMSE = 0.0443, NMSE = 7.5, MAPE = 3.13, and WAPE = 0.0318, is the most suitable for accurately estimating the CS of HPC. These findings highlight the efficacy of this hybrid model in HPC modeling and offer the potential for further research in the field.

关键词： High-performance concrete Compressive strength Support vector regression african vulture optimization algorithm Salp Swarm algorithm

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Multi video summarization using query based deep optimization algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第10期14卷 3591-3606页

作者： Ansari, Shaharyar Alam Zafar, Aasim Aligarh Muslim Univ Comp Sci Dept Aligarh India

The popularity of online video-sharing platforms has fuelled demand for systems that can quickly browse, extract, and summarise video information. Nowadays, numerous automatic multi-video summarization (MVS) techniques have come into existence. The existing MVS approach, on the other hand, produces summarised video with a lot of unimportant and duplicate frames. It also arranges frames in a meaningless manner. To solve these problems in MVS, Query-based Deep african vulture Learning (QDAVOL) is proposed in this paper. It uses tag information and web images searched by the query as important information to identify the query intent. An event-based object detection and grouping (EODG) technique is used to assign keyframes to groups of specific events relevant with the query. In addition, we introduce the african vulture optimization algorithm (AVOA) for the efficient key frame selection. Moreover, we have also developed a similarity-based frame closeness (SFC) technique to provide more comprehensible summary. Experimental results demonstrate that the proposed framework outperforms existing approaches in terms of precision (0.765), recall (0.845), and average F-score (0.774) on MVS1K dataset.

关键词： Multi-video summarization african vulture optimization algorithm Key frame selection Object detection Query based summarization

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Design and implementation of optimized virtual oscillatory controllers for grid-forming inverters

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ISA TRANSACTIONS 2023年第1期139卷 685-712页

作者： Gurugubelli, Vikash Ghosh, Arnab Panda, Anup Kumar Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 Odisha India

The percentage of renewable power in conventional power generation is gradually growing due to developments in power electronic converters (PECs). Renewable energy sources (RESs) can be integrated into the main grid through PECs, which are the most prevalent method to accomplish this. Virtual oscillator control (VOC) is a well-known time-domain method to regulate grid-forming inverters. VOC objective is to model the nonlinear dynamics of deadzone oscillator in a system of voltage source inverters to give a steady AC microgrid (MG). VOC is a self-synchronizing control method that just involves the current feedback signal. In contrast, classical droop and virtual synchronous machine (VSM) controllers both require the use of low pass filters to calculate real and reactive powers. The selection of control parameters in deadzone VOC is difficult and time-consuming. The VOC parameters are designed using different optimization techniques such as Particle Swarm optimization (PSO), Sine Cosine algorithm (SCA), modified Sine Cosine algorithm (mSCA), african vulture optimization algorithm (AVOA), and Artificial Jellyfish Search optimization (AJSO). MATLAB and a real-time digital simulator (Opal RT-OP5142) were used to examine the system's performance with aforesaid controllers (droop, VSM, conventional VOC, VOC-PSO, VOC-SCA, VOC-mSCA, VOC-AVOA, and VOCAJSO). In comparison to all control methods, the proposed VOC-AJSO provides faster synchronization. The effectiveness of the suggested VOC-AJSO control approach is proved by the hardware results.& COPY;2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： PSO SCA mSCA african vulture optimization algorithm Artificial jellyfish search optimization Droop control VSM VOC Islanded microgrid Parallel inverters

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Quantum-inspired african vultures optimization algorithm with elite mutation strategy for production scheduling problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第4期10卷 1767-1789页

作者： Liu, Bo Zhou, Yongquan Luo, Qifang Huang, Huajuan Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Univ Nationalities Xiangsihu Coll Nanning 530225 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

The production scheduling (PS) problem is a challenging task that involves assigning manufacturing resources to jobs while ensuring that all constraints are satisfied. The key difficulty in PS is determining the appropriate order of operations. In this study, we propose a novel optimization algorithm called the quantum-inspired african vultures optimization algorithm with an elite mutation strategy (QEMAVOA) to address this issue. QEMAVOA is an enhanced version of the african vulture optimization algorithm that incorporates three new improvement strategies. Firstly, to enhance QEMAVOA's diversification ability, the population diversity is enriched by the introduction of quantum double-chain encoding in the initialization phase of QEMAVOA. Secondly, the implementation of the quantum rotating gate will balance QEMAVOA's diversification and exploitation capabilities, leading the vulture to a better solution. Finally, with the purpose of improving the exploitability of QEMAVOA, the elite mutation strategy is introduced. To evaluate the performance of QEMAVOA, we apply it to two benchmark scheduling problems: flexible job shop scheduling problem and parallel machine scheduling. The results are compared to those of existing algorithms in the literature. The test results reveal that QEMAVOA surpasses comparison algorithms in accuracy, stability, and speed of convergence.

关键词： african vulture optimization algorithm elite mutation strategy quantum-inspired african vultures optimization algorithm parallel machine scheduling flexible job shop scheduling metaheuristic optimization

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