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A mixed Harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

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JOURNAL OF SUPERCOMPUTING 2023年第14期79卷 15270-15323页

作者： Zeng, Liang Li, Yanyan Zhang, Hao Li, Ming Wang, Shanshan Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Xiangyang Ind Inst Xiangyang 441100 Hubei Peoples R China

The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the Harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris' hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy.

关键词： Harris hawks optimization algorithm Pinhole imaging aquila optimization algorithm Flower pollination optimization

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An Automated Model for Child Language Impairment Prediction Using Hybrid Optimal BiLSTM

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IETE JOURNAL OF RESEARCH 2024年第1期70卷 291-306页

作者： Jaisharma, K. Deepa, N. Saveetha Inst Med & Tech Sci SIMATS Saveetha Sch Engn Dept Comp Sci & Engn Chennai India

Children without obvious disabilities (hearing loss/low intellectual capacity) may have language skill development issues due to specific language impairment (SLI), a communication disorder. The SLI has a significant impact on a child's speaking, listening, reading, and writing abilities. SLI is typically known as development language disorder, developmental dysphasia, or language delay. Recently, machine learning as well as deep learning techniques have been quite effective in predicting the early stage of SLI, analyzing the disorder severity, and predicting the treatment efficiency. Existing approaches primarily exploited auditory indicators to diagnose communication disorders, frequently leaving out hidden information acquired in the temporal domain. To overcome this drawback, an optimized Bidirectional Long Short Term Memory (BiLSTM) architecture is presented in this paper to handle the speech dynamics. The Improved Hybrid aquila Optimizer and Flow Directional algorithm known as IHAOFDA is integrated with the BiLSTM architecture to optimize the hyperparameters of the BiLSTM structure. When assessed using the information from the SLI children in the Laboratory of Artificial Neural Network Applications (LANNA) dataset, the proposed model performs better. The IHAOFDA-optimized BiLSTM architecture improves accuracy in classifying different severity levels such as mild, moderate, and severe.

关键词： aquila optimization algorithm Audio features Bidirectional long short term memory architecture Disorder severity Flow directional algorithm Specific language impairment Speech impairment

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Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems

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ALEXANDRIA ENGINEERING JOURNAL 2025年 110卷 490-505页

作者： Alotaibi, Sultan Refa Alkahtani, Hend Khalid Aljebreen, Mohammed Alshuhail, Asma Saeed, Muhammad Kashif Ebad, Shouki A. Almukadi, Wafa Sulaiman Alotaibi, Moneerah Shaqra Univ Dept Comp Sci Coll Sci & Humanities Dawadmi Dawadmi Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia King Faisal Univ Coll Comp Sci & Informat Technol Dept Informat Syst Al Hufuf Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Fac Sci Dept Comp Sci Ar Ar 91431 Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah Saudi Arabia

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with aquila optimization algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the aquila optimization algorithm (AOA) model. At last, the X

关键词： Cyber-physical system Feature selection Web phishing Artificial intelligence aquila optimization algorithm

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Optimizing PID control for maximum power point tracking in photovoltaic systems under variable and partial shading conditions

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RENEWABLE ENERGY 2025年 246卷

作者： Karuppasamy, C. Kumar, C. Senthil Ganesan, R. Elamparithi, P. AAA Coll Engn & Technol Dept Elect & Elect Engn Sivakasi 626005 India Kamaraj Coll Engn & Technol Dept Elect & Elect Engn Virudunagar 626001 India Ramco Inst Technol Dept Artificial Intelligence & Data Sci Rajapalayam 626117 India

The development of MPPT algorithms is crucial for optimizing energy utilization in photovoltaic systems. This manuscript introduces a hybrid method to enhance MPPT performance under partial shading conditions by optimizing PID control. The proposed AOA-DWNN method integrates the aquila optimization algorithm (AOA) for tuning PID gain settings and the Dynamic Wavelet Neural Network (DWNN) for predicting optimal converter parameters. This combination improves power tracking accuracy and optimizes energy utilization in varying environmental conditions. The method is put into practice in MATLAB and compared with existing methods, including the Salp Swarm algorithm and Sine Cosine algorithm (SSA-SCA), Firefly algorithm and Particle Swarm optimization (FA-PSO), and Artificial Bees Colony and Cuckoo Search algorithm (ABC-CSA). The results demonstrate a significantly lower error rate of 0.89 %, compared to 1.5 %, 1.2 %, and 1 % in existing approaches. Additionally, the proposed technique achieves an efficiency of 99.96 %, surpassing the 98.94 %, 97.95 %, and 96.94 % of other methods. The findings highlight the effectiveness of the AOA-DWNN technique in improving photovoltaic system performance, ensuring more accurate and reliable MPPT operation under partial shading conditions.

关键词： Dynamic wavelet neural network aquila optimization algorithm Photovoltaic PID controller Boost convertor Inverter

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Employing a support vector regression technique coupled with optimizers to estimate the compressive strength of reinforced concrete connections

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4503-4518页

作者： Yao, Hongmei Southwest Jiaotong Univ Dept Architecture & Civil Engn Hope Coll Chengdu 610400 Sichuan Peoples R China

Keeping stability and preventing collapse in reinforced concrete structures are remarked in most real and research projects. For immunizing structures, finding the logical relationship between mechanical features, like compressive strength and geometrical aspects of structures, would be an essential task. In this regard, the present research has aimed to develop a hybrid model by coupling a machine learning technique of Support Vector Regression with optimization algorithms instead of employing single frameworks of AI-based models in another research. To investigate the capability of models, 171 RC composite samples were considered in simulating the compressive strength of composite joints. As a result, developed models appeared more accurate than a single model of SVR without an optimizer. The capability of hybrid frameworks compared to a single model of SVR has been proved with better results of correlation index of R2 that there has been an average 10 percent difference between the accuracy of hybrid and single model. Also, using optimizers could reduce the RMSE error index average by 56 percent relative to a single SVR.

关键词： Reinforced concrete joints Cyclical compressive strength Column-beam connection Golden Jackal optimization aquila optimization algorithm

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Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2024年第4期35卷 463-487页

作者： Anbumozhi, Anna Shanthini, A. SRM Inst Sci & Technol Sch Comp Dept Data Sci & Business Syst Chennai 603203 India

Groundnut is a noteworthy oilseed crop. Attacks by leaf diseases are one of the most important reasons causing low yield and loss of groundnut plant growth, which will directly diminish the yield and quality. Therefore, an Optimized Wasserstein Deep Convolutional Generative Adversarial Network fostered Groundnut Leaf Disease Identification System (GLDI-WDCGAN-AOA) is proposed in this paper. The pre-processed output is fed to Hesitant Fuzzy Linguistic Bi-objective Clustering (HFL-BOC) for segmentation. By using Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN), the input leaf images are classified into Healthy leaf, early leaf spot, late leaf spot, nutrition deficiency, and rust. Finally, the weight parameters of WDCGAN are optimized by aquila optimization algorithm (AOA) to achieve high accuracy. The proposed GLDI-WDCGAN-AOA approach provides 23.51%, 22.01%, and 18.65% higher accuracy and 24.78%, 23.24%, and 28.98% lower error rate analysed with existing methods, such as Real-time automated identification and categorization of groundnut leaf disease utilizing hybrid machine learning methods (GLDI-DNN), Online identification of peanut leaf diseases utilizing the data balancing method along deep transfer learning (GLDI-LWCNN), and deep learning-driven method depending on progressive scaling method for the precise categorization of groundnut leaf infections (GLDI-CNN), respectively.

关键词： Adaptive and concise empirical wavelet transforms Anisotropic diffusion Kuwahara filtering aquila optimization algorithm deep learning groundnut leaf disease identification system Wasserstein Deep Convolutional Generative Adversarial Network

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A novel lithium-ion battery capacity prediction framework based on SVMD-AO-DELM

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第7期17卷 3793-3801页

作者： Ding, Guorong Chen, Hongxia Stat Bur Maiji Dist Tianshui 741020 Gansu Peoples R China Wuhan Univ Sci & Technol Coll Sci Wuhan 430065 Hubei Peoples R China

Accurate and efficient lithium-ion battery capacity prediction plays an important role in improving performance and ensuring safe operation. In this study, a novel lithium-ion battery capacity prediction model combining successive variational mode decomposition (SVMD) and aquila optimized deep extreme learning machine (AO-DELM) is proposed. Firstly, SVMD is used to divide capacity signal and it improves short-term trend prediction, especially for capacity growth that occurs during the degradation process. Secondly, the DELM network outperforms other networks in efficiently extracting time-dependent features, and it is more accurate than other standard ELM-based methods. The AO algorithm is used to find the parameters of the DELM training process for the problem of sensitivity to initial weights. Finally, experiments are conducted to validate the predictive performance of the proposed model based on NASA and CALCE lithium-ion batteries sequences. The MAE (0.0066Ah, 0.0044Ah), RMSE (0.0113Ah, 0.0078Ah), MAPE (0.44%, 0.82%) are effectively reduced, and the R-2 (98.94%, 99.87%) is better than the prediction performance of other hybrid models.

关键词： Lithium-ion battery capacity prediction Successive variational mode decomposition DELM aquila optimization algorithm

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Boosting multi-objective aquila optimizer with opposition-based learning for large-scale time–cost trade-off problems

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Asian Journal of Civil Engineering 2025年第5期26卷 2179-2188页

作者： Baltaci, Yusuf Civil Engineering Department Karadeniz Technical University Trabzon 61080 Turkey

This study presents an enhanced version of the aquila optimizer (AO), known as the opposition-based aquila optimizer (OBAO), which incorporates opposition-based learning (OBL) to enhance performance. By considering both current solutions and their opposites, OBL expands the search space, increasing the chances of avoiding local optima and identifying superior solutions. Additionally, OBL replaces the expanded and narrowed exploitation methods of the original AO, reducing computational complexity and enhancing the efficiency of the proposed model. The proposed OBAO is applied to a large-scale time–cost trade-off problems (TCTP) with 630 activities, demonstrating its capability to efficiently achieve optimal or near-optimal solutions. Comparative assessments against advanced optimization algorithms, including teaching learning-based optimization (TLBO), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and plain AO indicate that OBAO achieves better solutions in terms of number of objective function evaluations (NFE) and hypervolume (HV) indicator. The findings suggest that OBAO is a promising alternative for optimizing large-scale construction projects in construction management field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： aquila optimization algorithm Construction management Opposition-based learning Pareto-front solution Time–cost-trade-off problems

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Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM optimization algorithm

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PROCESSES 2023年第7期11卷 1957页

作者： Liu, Liqun Li, Yang Taiyuan Univ Sci & Technol Coll Elect & Informat Taiyuan 030024 Peoples R China

With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved aquila optimization algorithm (IAO) is used to optimize the parameters of the LSTM neural network to establish a model for predicting the actual photovoltaic power. The experimental results show that the proposed IAO-LSTM photovoltaic power prediction model has less error, and its overall quality and performance are significantly improved compared with the previously proposed AO-CNN model.

关键词： aquila optimization algorithm PV power prediction neural networks

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Fleet Load Charge Forecasting in Electric Vehicle Using Hybrid Deep Learning Model: LSTM-AQOA 1

Fleet Load Charge Forecasting in Electric Vehicle Using Hybr...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Kumar, M. Giridhar Kolla, Nagaraju Kotagi, Veeranna Mathapati, Mahantesh Kurnool India School of Management Studies Jawaharlal Nehru Technological University Anantapuram India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India Dept. of Cse Bengaluru India

ISBN: (纸本)9798350316469

When a high number of electric vehicles in a concentrated area are all charging at once from the same local transformer, the grid's performance, efficiency, and necessary capacity are all impacted. Thus, for a power system to function dependably and efficiently, precise load forecasting is essential. Charging loads from EV fleets are predicted to rise in importance to power grids. Therefore, anticipating the load from EV fleets is critical to ensuring the continued security and dependability of the electrical grid. In this study, we provide a novel hybrid EV fleet charging forecasting model based on multiple decomposition. Long short-term memory (LSTM) is one component of the artificial intelligence (AI) based forecasting model into which each deconstructed signal is input. aquila Optimisation algorithm (AQOA) is used to pick the best possible LSTM error threshold value in this case. The accuracy of the models is checked against actual charging data for EV fleets in the wild. Through a comparison of the deployed models, the prediction presentation of the projected model is measured using a variety of statistical measures. It is shown that, compared to models with only one decomposition, models with multiple decompositions perform better across a wide range of measures. © 2023 IEEE.

关键词： aquila optimization algorithm Electric Vehicle Fleet Loading Forecasting Long short-term memory Multiple decomposition model

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