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A boosted african vultures optimization algorithm combined with logarithmic weight inspired novel dynamic chaotic opposite learning strategy

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Chandran, Vanisree Mohapatra, Prabhujit Vellore Inst Technol Dept Math Vellore 632014 Tamil Nadu India

The african vultures optimization algorithm (AVOA), a newly developed swarm-intelligence meta-heuristics motivated by the scavenging and hunting behaviors of african vultures in the wild, has lately been extensively applied in many different domains. However, the AVOA still possesses significant drawbacks in solving challenging applications, including poor convergence accuracy, a lack of exploration ability, and being prone to local optima. To alleviate these drawbacks, a novel approach entitled "Boosted Dynamic Chaotic Opposite Learning (BDCOL)"technique is proposed and incorporated with AVOA, termed BDCOL-AVOA, to improve the performance of the AVOA. This study employs boosted dynamic chaotic opposite points to initialize the population and generation updating rather than opposite points. In BDCOL approach, an iterative- based logarithmic decreasing weight factor is introduced to regulate the complexity of the search domain, while the Chaotic Opposite Learning (COL) technique is implemented to systematically explore the search domain employing non-linear scaling behavior with the goal of a good trade-off between intensification and diversification of the algorithm. To evaluate the efficacy of the BDCOL-AVOA, it is implemented on a set of 23 classical CEC'05, 10 complex CEC'21, and 12 recently developed CEC'22 test functions, and its outcomes are statistically and graphically tested against the AVOA, along with several other meta-heuristics. In addition, statistical tests, notably the Friedman, Wilcoxon rank-sum, and t-tests, have been employed to exemplify the dominance of the BDCOL-AVOA. Furthermore, the BDCOL-AVOA is applied to several real-world engineering applications. The experimental findings have substantiated that BDCOL-AVOA has immense potential for addressing real-world engineering design problems.

关键词： Meta-heuristic algorithms african vultures optimization algorithm Opposition-based learning Chaotic opposite learning Boosted dynamic chaotic opposite learning

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african vultures optimization algorithm for optimization of shell and tube heat exchangers

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MATERIALS TESTING 2022年第8期64卷 1234-1241页

作者： Gurses, Dildar Mehta, Pranav Sait, Sadiq M. Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn Gorukle bursa TR-16059 Bursa Turkey Dharmsinh Desai Univ Dept Mech Engn Nadiad 387001 Gujarat India King Fahd Univ Petr Minerals Dhahran Saudi Arabia

Nature-inspired optimization algorithms named meta-heuristics are found to be versatile in engineering design fields. Their adaptability is also used in various areas of the Internet of things, structural design, and thermal system design. With the very rapid progress in industrial modernization, waste heat recovery from the power generating and thermal engineering organization is an imperative key point to reduce the emission and support the government norms. However, the heat exchanger is the component applied in various heat recovery processes. Out of the available designs, shell and tube heat exchangers (SHTHEs) are the most commonly adopted for the heat recovery process. Hence, cost minimization is the major aspect while designing the heat exchanger confirming various constraints and optimized design variables. In this study, cost minimization of the SHTHE is performed by applying a novel metaheuristic algorithm which is the african vultures optimization algorithm (AVOA). Adopting the AVOA for the best-optimized value (least cost of heat exchanger) and the design parameters are realized, confirming all the constraints. It was found that the AVOA is able to pursue the best results among the rest of them and can be used for the cost optimization of the plate-fin and tube-fin heat exchanger case studies.

关键词： african vultures optimization algorithm metaheuristics optimization Optimum design shell and tube heat exchangers

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Interpretable gradient boosting based ensemble learning and african vultures optimization algorithm optimization for estimating deflection induced by excavation

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Frontiers of Structural and Civil Engineering 2024年第11期18卷 1698-1712页

作者： Zenglong LIANG Shan LIN Miao DONG Xitailang CAO Hongwei GUO Hong ZHENG Key Laboratory of Urban Security and Disaster Engineering(Ministry of Education) Beijing University of TechnologyBeijing 100124China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University(PolyU)Hong Kong 999077China

Intelligent construction has become an inevitable trend in the development of the construction *** the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation *** interpretable gradient boosting based ensemble learning framework enhanced by the african vultures optimization algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by *** investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element ***,we exploratively analyzed these data to discover the relationship between *** used several state-of-the-art intelligent models based on gradient boosting and several simple models for model *** hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness *** comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.

关键词： african vultures optimization algorithm gradient boosting ensemble learning interpretable model wall deflection prediction

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An improved african vultures optimization algorithm using different fitness functions for multi-level thresholding image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第6期83卷 16929-16975页

作者： Gharehchopogh, Farhad Soleimanian Ibrikci, Turgay Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Adana Alparslan Turkes Sci & Technol Univ Dept Software Engn Adana Turkiye

Image segmentation is one of the most significant and required procedures in pre-processing and analyzing images. Metaheuristic optimization algorithms are used to solve a wide range of different problems because they can solve problems with different dimensions in an acceptable time and with quality results. It can show different functions in solving various problems. So, a metaheuristic algorithm should be adapted to solve the target problem with different mechanisms to find the best performance. In this paper, we have used the improved african vultures optimization algorithm (AVOA) that uses the three binary thresholds (Kapur's entropy, Tsallis entropy, and Ostu's entropy) in multi-threshold image segmentation. The Quantum Rotation Gate (QRG) mechanism has increased population diversity in optimization stages, and optimal local trap escapes to improve AVOA performance. The Association Strategy (AS) mechanism is used to obtain and faster search for optimal solutions. These two mechanisms increase the diversity of production solutions in all optimization stages because the AVOA algorithm focuses on the exploration phase almost in the first half of the iterations. So, in this approach, it is possible to guarantee a wide variety of solutions and avoid falling into the local optimum trap. Standard criteria and datasets were used to evaluate the performance of the proposed algorithm and then compared with other optimization algorithms. Eight images with large dimensions have been used to evaluate the proposed algorithm so that the ability of the proposed algorithm and other compared algorithms can be accurately checked. A better solution to large-scale problems requires good performance of the algorithm in both the exploitation and exploration phases, and a balance must be created between these two phases. According to the experimental results from the proposed algorithm, it is determined that it has a good and significant performance.

关键词： african vultures optimization algorithm Multi-level Thresholding Image Segmentation optimization

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IHAOAVOA: An improved hybrid aquila optimizer and african vultures optimization algorithm for global optimization problems

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第11期19卷 10963-11017页

作者： Xiao, Yaning Guo, Yanling Cui, Hao Wang, Yangwei Li, Jian Zhang, Yapeng Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China

Aquila Optimizer (AO) and african vultures optimization algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila and african vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising exploitation capability but insufficient exploration mechanisms. Based on the characteristics of both algorithms, in this paper, we propose an improved hybrid AO and AVOA optimizer called IHAOAVOA to overcome the deficiencies in the single algorithm and provide higher-quality solutions for solving global optimization problems. First, the exploration phase of AO and the exploitation phase of AVOA are combined to retain the valuable search competence of each. Then, a new composite opposition-based learning (COBL) is designed to increase the population diversity and help the hybrid algorithm escape from the local optima. In addition, to more effectively guide the search process and balance the exploration and exploitation, the fitness-distance balance (FDB) selection strategy is introduced to modify the core position update formula. The performance of the proposed IHAOAVOA is comprehensively investigated and analyzed by comparing against the basic AO, AVOA, and six state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Experimental results demonstrate that IHAOAVOA achieves superior solution accuracy, convergence speed, and local optima avoidance than other comparison methods on most test functions. Furthermore, the practicality of IHAOAVOA is highlighted by solving five engineering design problems. Our findings reveal that the proposed technique is also highly competitive and promising when addressing real-world optimization tasks. The source code of the IHAOAVOA is publicly available at https://***/10.24433/CO.2373662.v1.

关键词： aquila optimizer african vultures optimization algorithm hybrid algorithm composite opposition-based learning fitness-distance balance global optimization

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A multi-strategy enhanced african vultures optimization algorithm for global optimization problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第1期10卷 329-356页

作者： Zheng, Rong Hussien, Abdelazim G. Qaddoura, Raneem Jia, Heming Abualigah, Laith Wang, Shuang Saber, Abeer Putian Univ New Engn Ind Coll Putian 351100 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Fayoum Univ Fac Sci Faiyum 63514 Egypt Al Hussein Tech Univ Sch Comp & Informat Amman 11953 Jordan Al Al Bayt Univ Prince Hussein Bin Abdullah Coll Informat Technol Mafraq 130040 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Kafr El Sheikh Univ Fac Comp & Informat Dept Comp Sci Kafr Al Sheikh 33511 Egypt

The african vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the african vultures' behaviors. Though the basic AVOA performs very well for most optimization problems, it still suffers from the shortcomings of slow convergence rate and local optimal stagnation when solving complex optimization tasks. Therefore, this study introduces a modified version named enhanced AVOA (EAVOA). The proposed EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism, respectively, which are developed based on the basic AVOA. The representative vulture selection strategy strikes a good balance between global and local searches. The rotating flight strategy and selecting accumulation mechanism are utilized to improve the quality of the solution. The performance of EAVOA is validated on 23 classical benchmark functions with various types and dimensions and compared to those of nine other state-of-the-art methods according to numerical results and convergence curves. In addition, three real-world engineering design optimization problems are adopted to evaluate the practical applicability of EAVOA. Furthermore, EAVOA has been applied to classify multi-layer perception using XOR and cancer datasets. The experimental results clearly show that the EAVOA has superiority over other methods.

关键词： african vultures optimization algorithm global optimization engineering design problems metaheuristic exploration and exploitation multi-layer perception classification

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Design of a Resilient Wide-Area Damping Controller Using african vultures optimization algorithm 31

Design of a Resilient Wide-Area Damping Controller Using Afr...

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31st Australasian Universities Power Engineering Conference (AUPEC)

作者： Bento, Murilo E. C. Univ Fed Rio de Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9781665434515

Low-frequency oscillation modes when poorly damped can compromise the stable and continuous operation of power systems. The recent development of Wide-Area Measurement Systems allows data from Phasor Measurement Units (PMUs) to be used to compose a Wide-Area Damping Controller (WADC) and dampen these oscillation modes. However, the vulnerability of PMU data to cyber-attacks that can compromise the stability of the power system makes the implementation of the WADC difficult. This article proposes a WADC resilient design method based on an optimization problem. A metaheuristic called african vultures optimization algorithm will be used to solve the optimization problem. Case studies show the effectiveness of the designed controller in the dynamic performance of the system in continuous operation or under attack in its communication channels.

关键词： Smart Grid Cyber-security Power System Control Small-Signal Stability Wide-Area Damping Controller african vultures optimization algorithm

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Tuned african vultures optimization algorithm for Optimal Design of Skeletal Structures Employing Multi-Stage Parameter Adjustment

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第2期49卷 1211-1232页

作者： Kaveh, Ali Eskandari, Amir School of Civil Engineering Iran University of Science and Technology Narmak Tehran Iran

african vultures optimization algorithm (AVOA) is a nature-inspired optimization method whose inspiration corresponds to the behaviors of african vultures. This algorithm consists of some parameters that switch different phases, significantly influencing the performance. In this paper, the tuned version of this algorithm, termed TAVOA, with the use of Multi-Stage Parameter Adjustment (MSPA) is implemented for the size optimization of skeletal structures. The MSPA integrates Metaheuristics, Extreme Latin Hypercube Sampling (XLHS), and Machine Learning for metaheuristic parameter adjustment leading to a better performance in the context of structural optimization. AVOA and its adjusted version are tested against the sizing optimization of two space trusses and two frame structures, and the results are compared to those of some state-of-the-art algorithms and their improved or enhanced versions. The findings underscore the effectiveness of MSPA in enhancing the performance of AVOA when confronted with diverse optimization challenges, with a particular emphasis on its applicability for performing optimal designs of skeletal structures for both continuous and discrete optimization problems. © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： african vultures optimization algorithm Artificial Neural Networks Metaheuristics Multi-Stage Parameter Adjustment Optimal design of skeletal structures

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E-AVOA-TS: Enhanced african vultures optimization algorithm-based task scheduling strategy for fog-cloud computing

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2023年 40卷

作者： Ghafari, R. Mansouri, N. Shahid Bahonar Univ Kerman Dept Comp Sci Box 76135-133 Kerman Iran

In fog computing, inefficient scheduling of user tasks causes more delays. Moreover, how to schedule tasks that need to be offloaded to fog nodes or cloud nodes has not been fully addressed. The task scheduling process needs to be optimized and efficient in order to address the issues of resource utilization, response time, and energy consumption. This paper proposes an Enhanced african vultures optimization algorithm-based Task Scheduling Strategy (E-AVOA-TS) for fog-cloud computing. Through the proposed strategy, each village learns from its neighbors rather than from all of its members. The minimization of makespan, cost, and energy consumption in the proposed algorithm are considered as objective function. To prioritize tasks, the Best Worst Method (BWM) is used to handle the sensitivity of task delays. Latency-sensitive tasks are sent to the fog environment, while latency-tolerant tasks are sent to the cloud. E-AVOA is compared to other state-of-the-art optimizers using classic benchmark functions and ten benchmark tests from CEC-C06. Compared to other competitors, E-AVOA-TS outperforms makespan by 24.2%, cost by 16%, energy consumption by 4.7%, and DST% by 6.2% for large scale tasks. According to the simulation results, makespan shows improvements of 33%, 53%, and 48%, and energy consumption is reduced by 32%, 44%, and 5%, compared with PSG-M, IWC, and DCOHHOTS, respectively.

关键词： Fog -cloud computing Task scheduling Best Worst Method (BWM) african vultures optimization algorithm (AVOA) Dimension Learning -based Hunting (DLH)

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Classifying fabric wrinkle via regularized random vector function link optimized by african vultures optimization algorithm

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JOURNAL OF THE TEXTILE INSTITUTE 2024年

作者： Wen, Jingqi Zhou, Zhiyu Wang, Yaming Zhejiang Sci Tech Univ Sch Comp Sci & Technol Hangzhou Peoples R China Lishui Univ Zhejiang Key Lab DDIMCCP Lishui Peoples R China

The wrinkling of fabrics not only affects their aesthetic appeal and comfort but also leads to increased wear and a reduced lifespan. Consequently, wrinkle resistance has become a critical metric in evaluating fabric performance. Traditional wrinkle grading methods rely heavily on expert assessments, which are susceptible to visual fatigue, resulting in subjective and inconsistent classification outcomes. To address this issue, this article proposes an automatic wrinkle grading model based on an improved african vultures optimization (AVO) algorithm, which optimizes a regularized random vector functional link (RRVFL) network. The model enhances convergence speed and classification accuracy by incorporating the Harris Hawks optimization (HHO) algorithm to provide a superior initial population for the AVO algorithm. Additionally, the AVO algorithm's local search capability is strengthened during the optimization process, improving the model's stability and resistance to overfitting. Experimental results demonstrate that the proposed HHO-AVO-RRVFL model achieves an average classification accuracy of 97.86% across multiple datasets, significantly outperforming other comparative algorithms while also excelling in classification stability and convergence speed.

关键词： Fabric wrinkle objective evaluation Harris Hawks Optimizer african vultures optimization algorithm random vector functional link parameter optimization

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