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9th IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Jamal, Noor Zaihah Sulaiman, Mohd Herwan Aliman, Omar Mustaffa, Zuriani Mustafa, Mohd Wazir Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Fac Comp Syst & Software Engn Pekan 26600 Pahang Malaysia Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Johor Malaysia

ISBN: (纸本)9781538663219

Recently, nature inspired algorithms (NIA) have been implemented to various fields of optimization problem such as biomedical engineering, electrical engineering, computer science and etc. The achievement of NIA in solving optimization in these fields becoming the motivation to apply one of the NIA namely grey wolf Optimizer (GWO) into overcurrent relay coordination problem. However, the current state of GWO suffers lack of exploration problem. Hence, the improvement of GWO has been proposed in this paper to enhance the exploration of original GWO. The improvement of GWO (IGWO) is implemented in finding the the optimal value of the Time Multiplier Setting (TMS) and Plug Setting (PS) in order to minimize the primary relays' operating time at the near end fault. Comprehensive simulation studies have been performed to demonstrate the reliability and efficiency of the proposed modification technique compared to the original GWO. The generated results have confirmed the proposed IGWO is able to improve the objective function of the overcurrent relay coordination problem.

关键词： Time multiplier setting plug setting grey wolf optimization algorithm overcurrent relay coordination

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An adaptive elitism-based immigration for grey wolf optimization algorithm 2

An adaptive elitism-based immigration for grey wolf optimiza...

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International Conference on Digital Arts, Media and Technology (ICDAMT) - Digital Economy for Sustainable Growth

作者： Jitkongchuen, Duangjai Phaidang, Pongsak Pongtawevirat, Piyalak Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

ISBN: (纸本)9781509052103

This paper proposed the adaptive elitism-based immigration to improve the grey wolf optimization performance. The concept of elitism-based immigration is to generated immigrants and replaces it to the worst individuals in the population. The elite immigrants in our proposed are mutated before replace to the worst individuals and the parameter to control mutation ratio and elite immigrants ratio are adaptive. The performances have been evaluated by using 7 well-known benchmark functions and compared with the traditional grey wolf optimizer (GWO) algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithm. The experimental results showed that the proposed algorithm has ability to solving optimization problems.

关键词： Metaheuristic algorithm grey wolf optimization algorithm Elitism-base immigrants optimization problems

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Mobile robot navigation based on Fuzzy Cognitive Map optimized with grey wolf optimization algorithm used in Augmented Reality 5

Mobile robot navigation based on Fuzzy Cognitive Map optimiz...

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5th RSI International Conference on Robotics and Mechatronics (IcRoM)

作者： Malayjerdi, Ehsan Yaghoobi, Mahdi Kardan, Mohammad Islamic Azad Univ Mashhad Branch Dept Artificial Intelligence Mashhad Razavi Khorasan Iran Khayyam Univ Dept Comp Sci Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781538657034

this work presents a control technique for Mobile Robot Navigation using augmented reality (AR). This navigation technique is based on optimized Fuzzy Cognitive Map (FCM) and AR's Glyphs. AR's symbols are provided by the overhead camera. The patterns are made up of glyphs and a clear path. Six practical test are manipulated to examine the strength of optimizing FCM by a mobile robot for navigation with AR's symbols. The experiment examined the effectiveness of a grey wolf optimization algorithm (GWOA) in optimizing the FCM. Two practical experiments confirm that AR's Glyphs are an effective symbol for a robot to navigation in an unknown environment. A practical experiment reveals that a robot can use AR to manage its intended movement. Augmented reality, such as the Glyphs and a simplified map, are an effective tool for mobile robots to use in navigation in unknown environments. A prototype system is made to navigate the mobile robot by using AR and FCM.

关键词： Augmented reality Fuzzy Cognitive Map grey wolf optimization algorithm Mobile Robot Glyph

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Leveraging a novel grey wolf algorithm for optimization of photovoltaic-battery energy storage system under partial shading conditions

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 122卷

作者： Dagal, Idriss Ibrahim, AL-Wesabi Harrison, Ambe Beykent Univ Elect Engn Ayazaga MahallesiHadim Koruyolu Cd 9 Istanbul Turkiye Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Univ Buea Coll Technol COT Dept Elect & Elect Engn POB 63 Buea Cameroon

Photovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems can be significantly impacted by partial shading conditions, which can lead to power losses and reduced efficiency. This research proposes a novel grey wolf optimization algorithm (GWO) to optimize the operation of PV-BESS systems under partial shading conditions. The GWO, inspired by the hunting behavior of grey wolves, is a robust optimization technique capable of handling complex and nonlinear problems. The proposed approach aims to maximize energy output, minimize power losses, and ensure optimal battery management. By effectively addressing the challenges posed by partial shading, this research contributes to the advancement of PV-BESS systems as reliable and efficient renewable energy solutions. The proposed system, consisting of a PV array, boost converter, MPPT controller, and battery, was evaluated using MATLAB/Simulink under various conditions. The results demonstrate that the NGWO algorithm achieves 99.89 % tracking efficiency under standard conditions and over 99.26 % under PSC, outperforming particle swarm optimization (PSO), genetic algorithm (GA), the conventional GWO, and Perturb & Observe (P&O) methods. Notably, NGWO exhibits faster response times (0.01 s) and reduced power ripples compared to other algorithms, enhancing both energy extraction and battery efficiency. By optimizing state of charge (SOC) control, the NGWO extends battery lifespan, offering a superior solution for PV systems in challenging environments.

关键词： grey wolf optimization algorithm Partial shading conditions Energy storage Battery

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Application Research on Cable Force optimization of Cable-Stayed Bridge Based on Improved grey wolf algorithm

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BUILDINGS 2025年第8期15卷 1309-1309页

作者： Guo, Jian Zhu, Li Zhang, Yuzhong Guo, Hongzhe Lanzhou Jiaotong Univ Sch Civil Engn Lanzhou 730070 Peoples R China China Railway Third Engn Grp Sixth Engn Co Ltd Jinzhong 030600 Peoples R China Monash Univ Fac Informat Technol Clayton CampusWellington Rd Clayton Vic 3800 Australia

For complex structures, the solution process of existing cable force optimization methods for low-tower cable-stayed bridges is characterized by a significant number of matrix operations, which require substantial computing power and time. As a result, achieving a more accurate solution becomes exceedingly difficult. To tackle this challenge, we propose a new cable force optimization method that enhances the stress distribution of the cable-stayed cables in the completed state of the bridge. This approach minimizes the need for frequent adjustments to cable forces and alterations to the linear elevation of the beam bridge during construction. In this study, the low-tower cable-stayed bridge of the Lanjiang Bridge serves as the engineering background. By integrating finite element analysis with a multi-objective optimization method, we propose an optimization approach for the real-time correction of cable forces during the construction of long-span low-tower cable-stayed bridges. Within this optimization framework, the cable forces during construction are treated as variable parameters, while the linear elevation of the completed bridge is imposed as a constraint. The improved grey wolf algorithm is integrated with the finite element algorithm, and the key parameters of the support vector machine are optimized using this method, resulting in the optimal parameter combination predicted based on the training samples. The results indicate that after optimizing the support vector machine model using the improved grey wolf algorithm, the cable force distribution of the cable-stayed cables becomes more uniform, with a variance of 19.96. Additionally, the maximum displacement change of the main beam under the influence of the dead load is reduced by 33.48%. This method demonstrates high optimization efficiency and produces favorable outcomes, highlighting its value in calculating cable forces and guiding construction processes during the erection of cable-stayed cables for

关键词： support vector machine grey wolf optimization algorithm combination optimization method chaotic mapping

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Strengthened grey wolf optimization algorithms for numerical optimization tasks and AutoML

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Chen, Xuefen Ye, Chunming Zhang, Yang Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

The grey wolf optimization algorithm (GWO) is an efficient optimization technology. However, it still has some problems such as immature convergence and stagnation at local optima. In this paper, a strengthened grey wolf optimization algorithm (SGWO) is proposed based on three strengthening mechanisms: the exponential decreasing convergence factor, the elite reselection strategy in per generation and the Cauchy mutation (CM) operator. Seven variants of SGWO are designed according to different deployment modes of three reinforcement mechanisms. Experiments on thirteen numerical optimization problems are carried out to compare the differences between GWO and SGWOs. The experimental results reveal that SGWOs can significantly improve the search performance of GWO in most tasks. Among them, SGWO7 is the most successful competitor. Furthermore, several optimizers have demonstrated through comparison on engineering design problems that SGWO7 outperforms the vast majority of competitors. Subsequently, MHHO, TLBO, GWO and SGWO7 are used to build automatic machine learning (AutoML) model. The experimental results of the four methods on MNIST dataset further illustrate the advantages of SGWO7 designed in this research.

关键词： grey wolf optimization algorithm Metaheuristic Cauchy mutation AutoML

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Hybrid grey wolf Optimizer with Ant Lion optimization algorithm based Deep Learning for Face Expression Recognition 2

Hybrid Grey Wolf Optimizer with Ant Lion Optimization Algori...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jyoti Vishwanath, P. Alzubaidi, Laith H. Devika Rajesh, N. Raichur Visvesvaraya Technological University H.K.E Society's S.M.V College of Engineering Department of Ece Belagavi India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Kumarguru College of Liberal Arts and Science Department of Visual Communication Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Facial expressions are an important part of recognizing human emotional messages, hence it has been a focus of pattern recognition research. However, developments in convolutional neural networks and network topologies have resulted in difficulties such as decreased overall performance and the potential loss of crucial feature information in these methods. To overcome this issue, the hybrid optimization algorithm of grey wolf optimization algorithm (GWO) based Ant Lion optimization algorithm (ALO) termed as GWALO is proposed with deep learning for recognizing facial expressions. Viola Jones algorithm and Gabor Filtering are used in preprocessing to improve image quality by minimizing redundant distortion, allowing for the extraction of relevant information. The Affine-Scale-Invariant Feature Transform is used in feature extraction to extract expressions from a preprocessed image. The hybrid GWALO is utilized in the feature selection for selecting the better features. Recurrent Neural Network (RNN) of Deep Learning (DL) is utilized in classification for classifying the various facial expressions (happiness, joy, sorrow, angry, sad and so on). The obtained result shows that the proposed GWALO algorithm attains accuracy of 99.7% precision of 98.5%, recall of 97.8%, specificity of 99.6% and f1score of 96.7% that is better when contrasted to existing optimization algorithms. © 2024 IEEE.

关键词： Ant Lion optimization algorithm Facial Expression grey wolf optimization algorithm

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Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

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AD HOC NETWORKS 2024年 152卷

作者： Wang, Zhendong Huang, Lili Yang, Shuxin Luo, Xiao He, Daojing Chan, Sammy Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Ganzhou 341000 Jiangxi Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.

关键词： Hybrid higher-order coverage awareness model grey wolf optimization algorithm Obstacles Coverage optimization Network lifetime

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Design and investigation of PV string/central architecture for bayesian fusion technique using grey wolf optimization and flower pollination optimized algorithm

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期286卷

作者： Hemalatha, S. Renoald, A. Johny Banu, G. Indirajith, K. St Josephs Inst Technol Dept EEE Chennai Tamilnadu India Erode Sengunthar Engn Coll Dept EEE Perundurai Tamilnadu India VSB Coll Engn Tech Campus Dept EEE Coimbatore Tamilnadu India

One of the most essential factors in the current study is effectively harvesting the Maximum Power Extraction (MPE) from the Photovoltaic (PV) panel. The primary difficulties in extracting solar power is occurrence of partial shading which causes the panel to significantly increases power loss. These will mainly occur due to when partially shaded solar PV array kept under certain critical conditions for obtaining maximum output power. Many researcher have suggested by connecting bypass diodes in anti-parallel to the PV modules hotspots in the modules can be avoided. Out of all techniques, the proposed Bayesian Fusion Technique (BFT) is a hybrid optimization algorithm that combines the grey wolf optimization (GWO) and Flower Pollination algorithm (FPA) techniques to optimize the performance of solar panels in photovoltaic (PV) systems. The combination of GWO and FPA forms an ideal combination that is beneficial for optimizing the performance of PV systems is determined in this work. In this study real 6*6 PV array string and irregular PV array configuration such as central and parallel-series PV string combination of various partial shading pattern is compared and found to be effective for reducing the hotspots problems. The performance of these configuration under different shading patterns have been compared and analyzed with the different parameters like output power, conversion efficiency and tracking efficiency. This article state about the influence of partial darkening and the crucial point that reduce the sensitivity to shading heaviness. For better understanding for reader the MATLAB/Simulink software is used to validate the simulation result with real time data. Overall, this article states the BFT is an efficient and reliable approach to improve the efficiency of PV systems, by combining two optimization techniques like GWO and FPA hybrid algorithm. This article gives clear insight to the researchers for choosing BFTGWO algorithm in order to decrease the c

关键词： grey wolf optimization algorithm Flower Pollination algorithm Partially Shading Condition Bayesian Fusion Technique Maximum Power Point Photovoltaic array

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Elite-driven grey wolf optimization for global optimization and its application to feature selection

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Zhang, Li Chen, Xiaobo Jiangsu Univ Technol Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

Feature selection is crucial in data preprocessing, especially in medical data analysis. Although the grey wolf optimization (GWO) algorithm has attracted attention because of its simplicity and efficiency, it is prone to falling into the local optimum when searching fora globally optimal solution when dealing with complex feature selection problems, which restricts its application potential. To solve this problem, this paper proposes the Elite-driven grey wolf Optimizer (EDGWO) algorithm. The EDGWO algorithm significantly improves the global search capability of Alpha, Beta, and Delta grey wolves by taking advantage of the social hierarchy of the grey wolf population and designing three global exploration operators. The algorithm smoothly transitions from extensive exploration to intensive exploitation by dynamically adjusting the search parameters A. In addition, the introduced stochastic probabilistic search strategy allows omega grey wolves to make a flexible choice between local exploitation and global exploration, effectively avoiding premature convergence during the search process. To evaluate the performance of the EDGWO algorithm, this study compared twenty-two standard benchmark functions of CEC2021 and CEC2022 and twelve cancer microarray datasets. The experimental results show that the EDGWO algorithm demonstrates superior exploration and exploitation capabilities compared to fifteen well-known algorithms, with fast convergence speed and effective circumvention of local optima. Various evaluations have shown that EDGWO achieved the best Friedman rankings in the 10- and 20-dimensional CEC2021 and CEC2022 benchmark functions. In particular, the EDGWO algorithm maintains high convergence speed and high accuracy in feature selection for cancer microarray datasets.

关键词： Feature selection grey wolf optimization algorithm Elite-driven Global exploration Local exploitation Cancer microarrays

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