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Improved african vulture optimization algorithm Based on Quasi-Oppositional Differential Evolution Operator

IEEE ACCESS 2022年 10卷 95197-95218页

作者： Liu, Renju Wang, Tianlei Zhou, Jing Hao, Xiaoxi Xu, Ying Qiu, Jiongzhi Wuyi Univ Dept Intelligent Mfg Jiangmen 529020 Peoples R China

In this study, an improved african vulture optimization algorithm (IAVOA) that combines the african vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.

关键词： Statistics Sociology Mathematical models Particle swarm optimization Metaheuristics Convergence Benchmark testing african vulture optimization algorithm benchmark function differential evolution quasi-oppositional learning swarm intelligence algorithm

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Photovoltaic parameter estimation using honey badger algorithm and african vulture optimization algorithm

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ENERGY REPORTS 2022年 8卷 384-393页

作者： Diab, Ahmed A. Zaki Tolba, Mohamed A. El-Rifaie, Ali M. Denis, Kotin A. Minia Univ Fac Engn Elect Engn Dept Al Minya 61111 Egypt Atom Energy Author Reactors Dept Nucl Res Ctr Cairo 11787 Egypt Moscow Power Engn Inst Elect Power Syst Dept Moscow 111250 Russia Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Novosibirsk State Tech Univ Dept Elect Drive & Automat Ind Installat Novosibirsk Russia

The accurate arithmetical models of solar photovoltaic (PV) modules plays a vital role in the performance, operation, and control of the PV systems as a part of renewable generation. Therefore, the proposed paper develops a novel robust Honey Badger algorithm (HBA) and african vulture optimization algorithm (AVOA) that have been utilized for deducing the unknown parameters of various solar PV units accurately and efficiently. The HBA and AVOA techniques are developed to identify the approximate undefined parameters of different PV system models, namely, single diode model (SDM), double diode model (DDM), and three diode model (TDM). The attained results from the application of HBA and AVOA are to optimize the objective function of the current errors between the measure and the evaluation of the PV modules. Moreover, statistical tests have been presented to demonstrate the effectiveness of HBA and AVOA. The numerical results of the developed HBA and AVOA prove them as competitor methods for the parameters' extraction of solar PV systems. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： PV models Parameter evaluation Honey Badger algorithm african vulture optimization algorithm

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An Improved african vulture optimization algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews

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BIG DATA AND COGNITIVE COMPUTING 2022年第4期6卷 104页

作者： Shaddeli, Aitak Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Solouk, Vahid Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh *** Iran Urmia Univ Technol Fac Informat Technol & Comp Engn Orumiyeh *** Iran

The african vulture optimization algorithm (AVOA) is inspired by african vultures' feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model's performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis-IMDB, Amazon, and Yelp-show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.

关键词： african vulture optimization algorithm binary multi-strategies mutation neighborhood search strategy emotion analysis movie viewers

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Power System Harmonics Estimation: A new optimization technique-based implementation with african vulture optimization algorithm based Least Square Method 1

Power System Harmonics Estimation: A new optimization techni...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Ali, Atif Ahmed, Mushfiqur Apon, Hasan Jamil Rahman, Md. Samiur Ahsan, Auhona Prapti, Sumaiya Tasnim Ahmed, Ashik Islamic University of Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350342796

In present days, estimating harmonic components has become an important research area for conventional power system. Several stochastic optimization methods have been employed to successfully estimate the amplitude and phase of a harmonic signal. A novel optimization strategy must be implemented to increase precision and reduce the computational time. This research provides a technique for harmonics estimation based on the african vulture optimization algorithm (AVOA) that utilizes african vultures' natural hunting and navigational behavior. In this work, the amplitude of the harmonics of a test signal has been determined with the help of the traditional Least-Square (LS) approach, while the phase of the harmonics of that test signal has been determined using the african vulture optimization algorithm (AVOA). The results demonstrate that this technique can estimate the harmonic components more precisely. Finally, the results have been compared with other three optimization techniques proposed in previous works, such as Particle Swarm optimization with Passive Congregation (PSOPC) based least square method, Firefly algorithm (FA) based least square method, and Archimedes optimization algorithm (AOA) based least square method. The proposed AVOA-based least-square method outperforms all three algorithms regarding estimation precision. In single frequency estimation, at a 40 dB signal-to-noise ratio (Gaussian noise), the percentage error result between the estimated signal and the original signal is 1.0173 e-4, and the computational time is 0.0804 seconds. In case of multiple frequency estimation, with the same noise, the percentage error result is 1.07 e-4 and the computational time is 0.167 seconds. © 2023 IEEE.

关键词： african vulture optimization algorithm Harmonics Estimation Least Square Method Power Quality Power System Harmonics

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Optimized Back Propagation Neural Network Using Quasi-Oppositional Learning-Based african vulture optimization algorithm for Data Fusion in Wireless Sensor Networks

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SENSORS 2023年第14期23卷 6261-6261页

作者： Qaffas, Alaa A. Univ Jeddah Coll Business Dept MIS Jeddah 21589 Saudi Arabia

A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based african vulture optimization algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.

关键词： african vulture optimization algorithm back propagation neural network cluster-based routing data fusion quasi-oppositional learning wireless sensor networks

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Virtual synchronous generator based superconducting magnetic energy storage unit for load frequency control of micro-grid using african vulture optimization algorithm

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JOURNAL OF ENERGY STORAGE 2023年 65卷

作者： Rajaguru, V Annapoorani, K. Iyswarya Vellore Inst Technol Chennai Sch Elect Engn Chennai Tamil Nadu India Vellore Inst Technol Chennai Ctr Automat Chennai Tamil Nadu India

An isolated microgrid has significant frequency stability issues due to the erratic nature of renewable energy sources, stochastic load behaviour, and low system inertia. Consequently, this paper presents a Virtual Syn-chronous Generator (VSG) based Superconducting Magnetic Energy Storage (SMES) unit for frequency stability enhancement of microgrid. A newly developed metaheuristic algorithm named african vulture optimization algorithm (AVOA) is used to optimize the proportional-integral controller parameters with the help of the In-tegral Time Absolute Error criterion. The proposed Load Frequency Control (LFC) model of an isolated microgrid is evaluated against numerous scenarios and well-known algorithms like Genetic algorithm, Particle Swarm optimization, Grey Wolf optimization, and Harris Hawks optimization. The MATLAB simulation results of the proposed system demonstrate a significant improvement in the dynamic response pertaining to maximum overshoot, settling duration, and frequency oscillations when contrasted to the other algorithms. Also, observed that the LFC system with VSG-based SMES unit improves the frequency response of 77.37 % and 86.56 % in peak overshoot and settling time respectively as compared to the system without energy storage units. Finally, real-time simulations are carried out in OPAL-RT (OP-4500) to validate the proposed system.

关键词： african vulture optimization algorithm Load frequency control Micro-grid Superconducting magnetic energy storage unit Virtual synchronous generator

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An enhanced african vulture optimization algorithm for solving the Unmanned Aerial Vehicles path planning problem✩

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COMPUTERS & ELECTRICAL ENGINEERING 2023年第1期110卷

作者： Ait-Saadi, Amylia Meraihi, Yassine Soukane, Assia Yahia, Selma Ramdane-Cherif, Amar Gabis, Asma Benmessaoud Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Univ Paris Saclay LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France ECE Paris Sch Engn 37 quai Grenelle F-75015 Paris France Ecole Natl Super Lab Methodes Concept Syst Algiers 16309 Algeria

In recent years, research on Unmanned Aerial Vehicles has become one of the most interesting topics for industry and academics. UAVs path planning is one of the most important issues in terms of guaranteeing good performance in real-world applications. Its main objective is to determine and ensure an optimal and collision-free trajectory (path) between two positions from a starting point (source) to a destination point (target), while dealing with some requirements (e.g. safety, environment complexity, obstacle avoidance, energy consumption, etc.). In view of this topic's complexity, an efficient path planning algorithm is required. In this paper, we propose an improvement of the meta-heuristic african vulture optimization algorithm (AVOA), named Chaotic Cauchy Opposition-based AVOA (CCO-AVOA), for solving the UAVs path planning problem in a 3D environment. The effectiveness of the proposed CCO-AVOA is validated in different environments with various numbers of waypoints and threats taking into account the fitness value, path cost, height cost, obstacles cost, UAV's angle cost, and execution time metrics. Compared to ten well-known meta-heuristics, simulation results demonstrate the efficiency of the proposed CCO-AVOA approach in most cases by obtaining a short, smooth, least costly, and collision-free path with better stability for UAVs in complex environments.

关键词： UAV path planning Meta-heuristics african vulture optimization algorithm Elite Opposition-Based Cauchy mutation

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

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

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第43届中国控制会议

作者： Shanbin Li Qinglong Liu School of Automation Science and Engineering South China University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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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S-shaped and V-shaped binary african vulture optimization algorithm for feature selection

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EXPERT SYSTEMS 2022年第10期39卷

作者： Balakrishnan, Kulandaivel Dhanalakshmi, Ramasamy Seetharaman, Gopalakrishnan Indian Inst Informat Technol Tiruchirappalli 620012 Tamil Nadu India

The african vulture optimization algorithm (AVOA) is a recently developed metaheuristic algorithm that imitates the eating and movement patterns of authentic african vultures. AVOA is developed to address the continuous optimization problem. However, AVOA is unable to solve the discrete search space, this inspires us to develop the binary AVOA for feature selection problems in classification tasks. The suggested BAVOA incorporates an eight-transfer function (S-shaped and V-shaped) for transforming a continuous variable to a binary one. Using 14 benchmark data sets, the proposed technique is compared against 15 conventional binary metaheuristics algorithms in terms of classification accuracy, fitness function, number of selected features and converging ability. Furthermore, the results are statistically analysed using Wilcoxon test. The comparative findings of S-Shaped and V-shaped transfer functions indicate the superior performance of BAVOA methods, particularly S2-BAVOA, in contrast to other transfer function. Based on results, it turns out that the suggested technique converges to the global minimum in several iterations based on the selection of optimal characteristics, fitness values and higher classification accuracy as compared to the classical binary metaheuristic algorithms.

关键词： african vulture optimization algorithm feature selection meta-heuristic optimization

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Band power feature part-based convolutional neural network with african vulture optimization fostered channel selection for EEG classification

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年 1-14页

作者： Selvaraj, Vairaprakash Alagarsamy, Manjunathan Datchanamoorthy, Kavitha Manickam, Geethalakshmi Ramco Inst Technol Dept Elect & Commun Engn Rajapalayam Tamil Nadu India K Ramakrishnan Coll Technol Dept Elect & Commun Engn Trichy Tamil Nadu India Easwari Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Kongunadu Coll Engn & Technol Dept Elect & Commun Engn Trichy Tamil Nadu India

The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the optimal channel subset without severely impacting the classification performance is a problem in the field of BCI. To overwhelm this problem, a band power feature part-based convolutional neural network with african vulture optimization fostered channel selection for EEG classification (PCNNC-AVOACS-EEG) is proposed in this article. Initially, the input EEG signals are taken from BCI competition IV, dataset 1. Then the input EEG signals are pre-processed by contrast-limited adaptive histogram equalization filtering. These pre-processed EEG signals are extracted by hexadecimal local adaptive binary pattern (HLABP) method. This HLABP method extracts the features of alpha and beta bands from the EEG segments. Each EEG channel's band power data are utilized as features for a PCNNC to exactly classify the EEG into 3 classes: two MI states and idle state. The AVOA is applied within the band power feature PCNNC for channel selection, wherein channel selection aids to enhance the categorization accuracy on test set that is a vital indicator for real-time BCI applications. The proposed method is activated in python. From the experiment, the proposed technique attains 17.91%, 20.46% and 18.146% higher accuracy;14.105%, 15.295% and 5.291% higher area under the curve and 70%, 60% and 65.714% lower computation time compared with the existing approaches.

关键词： Contrast limited adaptive histogram equalization filtering african vulture optimization algorithm hexadecimal local adaptive binary pattern (HLABP) method electroencephalogram part-based convolutional neural network

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