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Workload Prediction in Cloud Data Centers Using Complex-Valued Spatio-Temporal Graph Convolutional Neural Network Optimized With gazelle optimization algorithm

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第3期36卷

作者： Karthikeyan, R. Samad, Saleem Raja Abdul Balamurugan, V. Balasubaramanian, Sundaravadivazhagan Cyriac, Robin Vardhaman Coll Engn Dept Comp Sci & Engn AI&ML Hyderabad Telangana India Univ Technol & Appl Sci Shinas Coll Comp & Informat Sci IT Dept Shinas Oman Mohamed Sathak Engn Coll Dept Informat Technol Kilakarai Tamil Nadu India Victoria Univ Fac Informat Technol Brisbane Campus Brisbane Australia

Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. In this manuscript, Workload Prediction in Cloud Data Centers using Complex-Valued Spatio-Temporal Graph Convolutional Neural Network Optimized with gazelle optimization algorithm (CVSTGCN-WLP-CDC) is proposed. Initially, the input data is collected from two standard datasets such as NASA and Saskatchewan HTTP traces dataset. Then, preprocessing using Multi-Window Savitzky-Golay Filter (MWSGF) is used to remove noise and redundant the data. The preprocessed data is fed to CVSTGCN for workload prediction in a dynamic cloud environment. In this work, proposed gazelle optimization Approach (GOA) used to enhance the CVSTGCN weight and bias parameters. The proposed CVSTGCN-WLP-CDC technique is executed and efficacy based on workload prediction structure is evaluated using several performances metrics such as accuracy, recall, precision, energy consumption correlation coefficient, sum of elasticity index (SEI), root mean square error (RMSE), mean squared prediction error (MPE), and percentage prediction error (PER). The proposed CVSTGCN-WLP-CDC provides 23.32%, 28.53% and 24.65% higher accuracy;22.34%, 25.62%, and 22.84% lower energy consumption when comparing to the existing methods using Artificial Intelligence augmented evolutionary approach espoused cloud data centres workload prediction architecture (TCNN-CDC-WLP), Performance analysis of machine learning centered workload prediction techniques for cloud (PA-BPNN-CWPC), Machine learning methods for effectual energy utilization in cloud data centers (ARNN-EU-CDC) methods respectively.

关键词： cloud data Centres complex-value spatio-temporal graph convolutional neural network gazelle optimization algorithm Multi-Window Savitzky-Golay Filter work load prediction

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Convolution neural network-AlexNet with gazelle optimization algorithm-based software defect prediction

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KNOWLEDGE AND INFORMATION SYSTEMS 2025年 1-22页

作者： Devi, S. V. Gayetri Ranjith, V. G. Ramani, P. Kavitha, A. Aalim Muhammed Salegh Coll Engn Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumarcoil Tamil Nadu India SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai 600089 India K Ramakrishnan Coll Technol Dept Elect & Commun Engn Trichy Tamil Nadu India

Nowadays, the size and complexity of software systems have increased dramatically. Software defects are very challenging to prevent because of these characteristics. Therefore, developers may be able to better allocate their limited resources by predicting the number of defects in software modules automatically. There are various approaches presented for identifying and fixing such problems, but none of these give sufficient results. To address these, this paper proposes convolution neural network-AlexNet with gazelle optimization algorithm-based software defect prediction (SWDP-CNN-AlexNet-GAOA). Here, NASA software defect prediction dataset is used. The feature normalization ensures that all features contribute equally to the model. Without normalization, features with larger numerical values would dominate the learning process. Then, software defects are predicted using convolution neural network (CNN)-AlexNet. Finally, gazelle optimization algorithm (GAOA) is proposed to optimize the parameters of CNN-AlexNet. Simulation proves that the SWDP-CNN-AlexNet-GAOA method outperforms existing models. The proposed SWDP-CNN-AlexNet-GAOA approach attains 3.88%, 5.75%, and 4.94% better accuracy and 6.25%, 5.91%, and 11.28% better F-measure compared with the existing methods, like software defect prediction using enhanced CNN (SWDP-EN-CNN), software defect prediction using hybrid swarm intelligence and deep learning (SWDP-HS-DL), and software defect prediction under ant colony optimization (SWDP-ACO), respectively.

关键词： Convolution neural network Deep learning Feature selection gazelle optimization algorithm Neural network Software testing Software defect prediction

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Harmonic estimator design using improved gazelle optimization algorithm by chaos for real field signal

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Saka, Mustafa Coban, Melih Iskenderun Tech Univ Fac Engn & Nat Sci Dept Elect & Elect Engn TR-31200 Hatay Turkiye Bolu Abant Izzet Baysal Univ Engn Fac Dept Elect Elect Engn TR-14230 Bolu Turkiye

One of the most important issues affecting electrical power quality is the harmonics that occur in electrical signals. Harmonics cause many negative effects such as loss of electricity, overheating of devices, interference between equipment, and decreased system efficiency. Therefore, estimating the harmonics of signals is necessary for the power system quality. In this study, gazelle optimization algorithm (GOA) improved with chaotic maps is proposed for the estimation of harmonic and sub/inter-harmonic signals. The performances of ten different chaos-based GOAs have been analyzed for 2 synthetic power signals and a time varying signal. Also, noisy (10 dB and 20 dB Signal to Noise Ratio (SNR)) and non-noisy cases of the signals have been considered. Moreover, the obtained performances have been examined both statistically and Wilcoxon Sign Rank Test (WSRT) method. Gauss/Mouse chaotic map based GOA (G/M GOA) has been given better results than other chaos-based methods and therefore it has been applied for the estimation of a real field signal. The amplitude, frequency and phase parameters of the signals have been estimated and the percentage error values of these parameters have been computed in all performed analyzes. From the obtained results for different cases, it is achieved that G/M GOA estimate the both synthetic and real harmonic and sub/inter-harmonic signal parameters with high accuracy. (c) 2017 Elsevier Inc. All rights reserved.

关键词： Harmonics Harmonic estimation Chaos map gazelle optimization algorithm

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Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With gazelle optimization algorithm

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第4期36卷

作者： Barona, R. Raj, E. Babu St Xaviers Catholic Coll Engn Dept Comp Sci & Engn Nagercoil India Christ Univ Dept Comp Sci & Engn Bengaluru India

The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability of the current network, especially in Internet of Things (IoT) and cloud environments. Traditional detection methods often struggle with the inability to achieve a balance between detection accuracy and computational efficiency. In this manuscript, the Classification of Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized with gazelle optimization algorithm (DDOS-AD-BWRF-GOA) is proposed. First, the raw data is gathered from the CICDDoS2019 dataset. Then, input data are preprocessed utilizing Adaptive Bitonic Filtering for normalizing the values. The preprocessed data are fed to the Improved Feed Forward Long Short-Term Memory technique for selecting features that increase the model's execution time. The selected features are supplied to the Bayesian Weighted Random Forest (BWRF), which classifies the multiclass DDOS attack. In general, Bayesian Weighted Random Forest does not adopt any optimization methods to define optimal parameters to guarantee exact DDOS identification. Hence, GOA is proposed to optimize the Bayesian Weighted Random Forest classifier. The proposed method is implemented in MATLAB. The performance metrics, such as Accuracy, Precision, Recall, F1-score, Specificity, Error rate, and Computational time are evaluated. The proposed method attains 15.34%, 24.1%, and 18.9% higher accuracy and 12.4%, 18.24%, and 22.6% higher precision when analyzed with existing techniques: Hybrid deep learning method for DDOS detection and classification (HDL-DDOS-DC), Edge-HetIoT Defense against DDoS attack utilizing learning techniques (EHD-DDOS-LT), and Digital twin-enabled intelligent DDOS detection for autonomous core networks (DTI-DDOS-ACN), respectively.

关键词： adaptive bitonic filtering bayesian weighted random forest gazelle optimization algorithm improved feed forward-long short-term memory method raw data

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gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer

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NEURAL COMPUTING & APPLICATIONS 2023年第5期35卷 4099-4131页

作者： Agushaka, Jeffrey O. Ezugwu, Absalom E. Abualigah, Laith Univ KwaZulu Natal Sch Math Stat & Comp Sci King Edward Rd ZA-3201 Kwa Zulu South Africa Fed Univ Lafia Dept Comp Sci Lafia 950101 Nigeria Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

This study proposes a novel population-based metaheuristic algorithm called the gazelle optimization algorithm (GOA), inspired by the gazelles' survival ability in their predator-dominated environment. Every day, the gazelle knows that if it does not outrun and outmaneuver its predators, it becomes meat for the day, and to survive, the gazelles have to escape from their predators consistently. This information is vital to proposing a new metaheuristic algorithm that uses the gazelle's survival abilities to solve real-world optimization problems. The exploitation phase of the algorithm simulates the gazelles grazing peacefully in the absence of the predator or while the predator is stalking it. The GOA goes into the exploration phase once a predator is spotted. The exploration phase consists of the gazelle outrunning and outmaneuvering the predator to a safe haven. These two phases are iteratively repeated, subject to the termination criteria, and finding optimal solutions to the optimization problems. The robustness and efficiency of the developed algorithm as an optimization tool were tested using benchmark optimization test functions and selected engineering design problems (fifteen classical, ten composited functions, and four mechanical engineering design problems). The results of the GOA are compared with nine other state-of-the-art algorithms. The simulation results obtained confirm the superiority and competitiveness of the GOA algorithm over nine state-of-the-art algorithms available in the literature. Also, the standard statistical analysis test carried out on the results further confirmed the ability of GOA to find solutions to the selected optimization problems. It also showed that GOA performed better or, in some cases, was very competitive with some state-of-the-art algorithms. Also, the results show that GOA is a potent tool for optimization that can be adapted to solve problems in different optimization domains.

关键词： gazelle optimization algorithm GOA Metaheuristics optimization problems Industrial engineering design problems Nature-inspired gazelle Population-based

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gazelle optimization algorithm with Multi-strategy and Its Application 17

Gazelle Optimization Algorithm with Multi-strategy and Its A...

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17th International Conference on Advanced Computer Theory and Engineering

作者： Gong, Honghao Deng, Yuxuan Xia, Kewen Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China

ISBN: (纸本)9798331539894;9798331539887

gazelle optimization algorithm (GOA) is a heuristic optimization algorithm based on gazelle behavior in nature. Inspiration comes from the gazelle's ability to survive in predator-dominated environments, and the gazelle's ability to maintain high speed and flexibility when running across the savannah inspired the design of the gazelle optimization algorithm. However, due to its shortcomings of falling into local minima and slow convergence, a gazelle optimization algorithm with Multi-strategy(TLGOA) is proposed. In the initial stage, tent chaotic map is added to initialize the population, and then the refraction reverse learning strategy is used to improve the diversity and quality of the population, which can effectively improve the convergence speed of the algorithm. Finally, the optimization method of golden section number optimization GOA is introduced to coordinate the global exploration and local development ability of the algorithm. Through testing and comparative analysis of various international benchmark functions, the results show that TLGOA has obvious convergence speed and conversion accuracy. Finally, TLGOA is used to solve the Traveling salesman problem (TSP), and the results are remarkable.

关键词： gazelle optimization algorithm Tent Chaotic Map Golden Sine Refraction Reverse Learning Traveling Salesman Problem

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Computer Aided Cervical Cancer Diagnosis Using gazelle optimization algorithm With Deep Learning Model

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IEEE ACCESS 2024年 12卷 13046-13054页

作者： Nour, Mohamed K. Issaoui, Imene Edris, Alaa Mahmud, Ahmed Assiri, Mohammed Ibrahim, Sara Saadeldeen Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca 21421 Saudi Arabia Qassim Univ Appl Coll Unit Sci Res Buraydah 52571 Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Dept Comp Sci & Artificial Intelligence Jeddah 23890 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Aflaj Dept Comp Sci Aflaj 16273 Saudi Arabia

Cervical cancer (CC), the most common cancer among women, is most commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes a promising tool for improving CC diagnosis. Deep learning (DL), a branch of AI, holds particular potential in CAD systems for early detection and accurate diagnosis. DL algorithm is trained to identify abnormalities and patterns in Pap smear images, such as dysplasia, cellular changes, and other markers of CC. So, this study presents a Computer Aided Cervical Cancer Diagnosis utilizing the gazelle Optimizer algorithm with Deep Learning (CACCD-GOADL) model on Pap smear images. The foremost objective of the CACCD-GOADL approach is to examine the image detection of CC. To accomplish this, the CACCD-GOADL methodology uses an improved MobileNetv3 model for extracting complex patterns in Pap smear images. In addition, the CACCD-GOADL technique designs a new GOA for the hyperparameter tuning of the improved MobileNetv3 system. For the classification and identification of cancer, the CACCD-GOADL technique uses a stacked extreme learning machine (SELM) methodology. The simulation validation of the CACCD-GOADL approach is verified on a benchmark dataset of Herlev. Experimental results highlighted that the CACCD-GOADL algorithm reaches superior outcomes over other methods.

关键词： Feature extraction Solid modeling Classification algorithms Cervical cancer optimization Medical diagnostic imaging Computer aided diagnosis Deep learning Machine learning gazelle optimization algorithm computer-aided diagnosis deep learning machine learning

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An improved gazelle optimization algorithm using dynamic opposition-based learning and chaotic mapping combination for solving optimization problems

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JOURNAL OF SUPERCOMPUTING 2024年第9期80卷 12813-12843页

作者： Abdollahpour, Atiyeh Rouhi, Alireza Pira, Einollah Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz *** East Azarbaijan Iran

The gazelle optimization algorithm (GOA) is an iterative optimization method inspired by the agile movements of gazelles, employing adaptive step sizes and velocity adjustments for rapid convergence in continuous search spaces. However, GOA tends to lack diversity, leading to issues like local minima trapping and premature convergence. This paper addresses these limitations by introducing dynamic opposition-based learning (OBL) and incorporating a balanced sine-control-logistic chaotic mapping system, resulting in the improved GOA (IGOA). Dynamic OBL accelerates the search process, improving learning and selecting superior candidate solutions, while chaotic mapping in chaotic local search widens ranges for exploration and local optima escape. Evaluating IGOA against seven algorithms, including GOA and others, across 31 general test functions, the results consistently showcase IGOA's superior efficiency in achieving solutions closest to optima, early convergence, and hit rate when compared to alternative algorithms.

关键词： Dynamic opposition-based learning gazelle optimization algorithm Dynamic chaotic local search Metaheuristic algorithm Hit rate

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Parameters optimization of PEMFC model based on gazelle optimization algorithm

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 87卷 214-226页

作者： Haddad, Sofiane Benghanem, Mohamed Hassan, Belqees Soukkou, Ammar Lekouaghet, Badis Soukkou, Yassine Univ Jijel Fac Sci & Technol Dept Elect Jijel 18000 Algeria Islamic Univ Madinah Fac Sci Dept Phys Madinah 42351 Saudi Arabia Qassim Univ Coll Sci Dept Phys Buraydah 51452 Saudi Arabia Res Ctr Ind Technol CRTI POB 64Cheraga Algiers 16014 Algeria

Fuel cells (FCs) play a crucial role in converting stored hydrogen energy into electricity. However, accurately modeling and optimizing their performance is challenging due to the lack of critical parameter data-specifically, seven key parameters including four semi-empirical coefficients (xi 1, xi 2, xi 3, xi 4), an adjusting parametric coefficient (beta), a constant equivalent resistance (Rc), and an adjustable parameter (lambda). These essential parameters are typically not provided in manufacturers' datasheets, creating a significant gap in the precise calibration and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs). This study addresses this gap by applying seven population-based meta-heuristic algorithms to estimate and optimize these unknown parameters. Among these, the gazelle optimization algorithm (GOA) is identified as particularly effective, offering superior precision and rapid convergence. Our research evaluates the performance of these algorithms using indicators such as Standard Deviation (StD) and Sum of Squared Errors (SSE). The GOA achieved exceptionally low SSE values of 7.637606 x 10<^>-3, 1.28694222 x 10(-2), and 2.288128 for the Horizon 500W, BCS 500W, and NedStack PS6 stacks, respectively, along with corresponding StD values of 2.275703 x 10(-9), 9.12077649 x 10(-15), and 3.26518838 x 10(-14). These results underscore the algorithm's accuracy and effectiveness in optimizing PEMFC parameters, closely aligning with the manufacturers' polarization curves. The study's findings, validated across these three different fuel cell stacks, highlight the GOA's superiority over other methods in terms of accuracy and convergence speed. This manuscript contributes to the field by providing a robust method for accurately optimizing PEMFC parameters, which are critical for enhancing the overall performance of fuel cells. The results also demonstrate the GOA's potential as a superior optimization tool in the field of fuel cell technology.

关键词： gazelle optimization algorithm BCS 500W stack Horizon 500 NedStack PS6 stack optimization algorithms PEMFCs parameters estimation

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Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Abdel-Salam, Mahmoud Askr, Heba Hassanien, Aboul Ella Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Univ Sadat City Fac Comp & Artificial Intelligence Menoufia Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt Kuwait Univ Coll Business Adm CBA Kuwait Kuwait

Feature Selection (FS) is considered a crucial procedure for eliminating unnecessary features from datasets. FS is considered a challenging problem that is difficult to solve efficiently due to its combinatorial nature. As the problem size increases, so does the computation time. Recently, researchers have been focusing on metaheuristic FS algorithms. Therefore, this paper introduces an adaptive chaotic dynamic gazelle optimization algorithm (ACD-GOA) with enhanced elite Strategy for FS problems. The ACD-GOA is a novel enhanced version of the recently published GOA, incorporating multiple improved strategies to enhance its search capabilities and convergence speed. The initialization phase and iteration initialization adopt the dynamic opposition learning strategy to avoid premature convergence. Additionally, several enhancement strategies are employed to improve the efficiency of the standard GOA. Adaptive inertia weight and sigmoid function are used to enhance search efficiency. Furthermore, enhanced elite and exchanging information strategies are implemented to maintain population diversity and avoid local solutions, respectively. Experimental evaluations are conducted using various functions, including the twelve CEC2022 standard functions and fourteen FS benchmark datasets. The performance of ACD-GOA is compared with several other metaheuristic algorithms. Statistical tests such as Friedman and Wilcoxon signed-rank tests are used to analyze the experimental data. The experimental results highlight the proposed algorithm's exceptional capacity to overcome problems associated with local minima and expedite the convergence process. The suggested algorithm has been extensively compared to state-of-the-art algorithms, demonstrating significant breakthroughs. The reported accuracy of the proposed algorithm ranges from 0.78 to 1.00 with K-NN classifier.

关键词： Feature Selection gazelle optimization algorithm Dynamic Opposition Learning Chaotic Mapping Machine Learning Classification Friedman Wilcoxon Tests

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