Relevant bibliographies by topics / Gray wolf optimizer

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gray wolf optimizer'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Gray wolf optimizer"

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Medjahed, S. A., T. Ait Saadi, A. Benyettou, and M. Ouali. "Gray Wolf Optimizer for hyperspectral band selection." Applied Soft Computing 40 (March 2016): 178–86. http://dx.doi.org/10.1016/j.asoc.2015.09.045.

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Jiang, Hao, Jiuxiang Song, Baowei Zhang, and Yonghua Wang. "Yarn unevenness prediction using generalized regression neural network under various optimization algorithms." Journal of Engineered Fibers and Fabrics 17 (January 2022): 155892502210930. http://dx.doi.org/10.1177/15589250221093019.

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Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiv
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ALJRIBI, Salem Faraj, and Ziyodulla YUSUPOV. "Gray Wolf, Mikro Şebekede Pil Depolama Dahil Optimize Edilmiş Ekonomik Yük Dağıtımı." Journal of Polytechnic 27, no. 1 (2021): 27–33. http://dx.doi.org/10.2339/politeknik.886712.

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In last decades, grey wolf optimizer algorithm as a new meta-heuristic optimization technique plays major role in optimization of engineering problems such as load forecasting, controller parameter tuning and job scheduling. In this paper, grey wolf optimization (GWO) is used to optimize the microgrid system for effective dispatching of power to load with economic manner. The model of microgrid system components are developed and investigated in the MATLAB/Simulink platform. The vital objective of the proposed grey wolf algorithm is to minimize overall cost of the microgrid operation. The deta
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Kumar, Anuj, Sangeeta Pant, and Mangey Ram. "System Reliability Optimization Using Gray Wolf Optimizer Algorithm." Quality and Reliability Engineering International 33, no. 7 (2016): 1327–35. http://dx.doi.org/10.1002/qre.2107.

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Puteri Baharie, Sri Rossa Aisyah, Sugiyarto Surono, and Aris Thobirin. "Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 9, no. 1 (2025): 146–52. https://doi.org/10.29207/resti.v9i1.6203.

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Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, its performance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes gradient descent
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Kumar, Vikas, Kanak Kalita, S. Madhu, Uvaraja Ragavendran, and Xiao-Zhi Gao. "A Hybrid Genetic Programming–Gray Wolf Optimizer Approach for Process Optimization of Biodiesel Production." Processes 9, no. 3 (2021): 442. http://dx.doi.org/10.3390/pr9030442.

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Biodiesel is one the most sought after alternate fuels in the current global need for sustainable and renewable energy sources due to their lower emissions and no major modification requirement to existing engines. However, the performance and productivity of the biodiesel production process are significantly dependent on the process parameters. In this regard, a novel hybrid genetic programming-gray wolf optimizer approach for the process optimization of biodiesel production is proposed in this paper. For an illustration of the proposed approach, kinematic viscosity is expressed as a symbolic
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Knypiński, Łukasz. "Constrained optimization of line-start PM motor based on the gray wolf optimizer." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 1 (2021): 1–10. http://dx.doi.org/10.17531/ein.2021.1.1.

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This paper presents the algorithm and computer software for constrained optimization based on the gray wolf algorithm. The gray wolf algorithm was combined with the external penalty function approach. The optimization procedure was developed using Borland Delphi 7.0. The developed procedure was then applied to design of a line-start PM synchronous motor. The motor was described by three design variables which determine the rotor structure. The multiplicative compromise function consisted of three maintenance parameters of designed motor and one non-linear constraint function was proposed. Next
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Ahmed, Sapan, and Dleen Salih. "IHS Image Fusion Based on Gray Wolf Optimizer (GWO)." Anbar Journal for Engineering Sciences 13, no. 1 (2022): 65–75. http://dx.doi.org/10.37649/aengs.2022.175882.

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Jia, Zhengzhao, Ziling Song, Junfu Fan, and Juyu Jiang. "Prediction of Blasting Fragmentation Based on GWO-ELM." Shock and Vibration 2022 (January 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/7385456.

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Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragm
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Masoumi, Fariborz, Sina Masoumzadeh, Negin Zafari, and Mohammad Javad Emami-Skardi. "Optimum Sanitary Sewer Network Design Using Shuffled Gray Wolf Optimizer." Journal of Pipeline Systems Engineering and Practice 12, no. 4 (2021): 04021055. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000597.

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Dissertations / Theses on the topic "Gray wolf optimizer"

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Bhandare, Ashray Sadashiv. "Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural Networks." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273210921513.

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Lakshminarayanan, Srivathsan. "Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438102173.

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Yarlagadda, Rahul Rama Swamy. "Inverse Modeling: Theory and Engineering Examples." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1449724104.

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Alatram, Ala'a A. M. "A forensic framework for detecting denial-of-service attacks in IoT networks using the MQTT protocol." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2561.

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In the domain of the Internet of Things (IoT), The Message Queueing Telemetry Protocol (MQTT) is the most widely used protocol for applications across a wide range of realms, including industrial automation, healthcare, smart homes, and smart cities; MQTT is also used in many other critical real-world applicastions. An example is BMW’s Car Sharing application, that uses MQTT to provide reliable connectivity. However, due to a lack of security considerations during the design of the MQTT protocol, all the networks implementing it are prone to cyberattacks, such as denial-of-service (DoS) attack
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Wei-HsinYen and 顏維信. "Enhanced Grey Wolf Optimizer based Multiple Object Grasping Poses for Home Service Robot." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/64688212675751809429.

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碩士<br>國立成功大學<br>電機工程學系<br>104<br>The thesis proposes an Enhanced Grey Wolf Optimizer (EGWO) to learn multiple grasping poses of unknown objects for a home service robot. For accomplishing this task, a 3D model of an object should be established at first. The depth information obtained from Kinect is converted to 3D points in every frame and is matched by Iterative Closest Point (ICP) to track the pose of Kinect. The matched points are then integrated to a volumetric surface, and the result is presented by ray casting. However, the result of 3D object model is too complex to calculate grasping
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Javidsharifi, M., T. Niknam, J. Aghaei, Geev Mokryani, and P. Papadopoulos. "Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm." 2018. http://hdl.handle.net/10454/16610.

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Yes<br>Investigation of the environmental/economic optimal operation management of a microgrid (MG) as a case study for applying a novel modified multi-objective grey wolf optimizer (MMOGWO) algorithm is presented in this paper. MGs can be considered as a fundamental solution in order for distributed generators’ (DGs) management in future smart grids. In the multi-objective problems, since the objective functions are conflict, the best compromised solution should be extracted through an efficient approach. Accordingly, a proper method is applied for exploring the best compromised solution. Add
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HU, SHIH-YING, and 胡世穎. "Design and Implementation of MPPT Controller for Photovoltaic Power Generation with Grey Wolf Optimizer." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/42963042724151183949.

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碩士<br>國立臺南大學<br>電機工程學系碩博士班<br>105<br>In recent years, increasing numbers of photovoltaic (PV) generation system have entered the market. However with the current engineering technology, including solar panels, shading, circuit issues, it still causes a lot of energy losses in the generation process, resulting in direct economic benefits of the overall system that cannot competed with the traditional system. Thus, in this thesis, it is aimed to develop a novel circuit method to improve the efficiency of the photovoltaic generation operation. Resulting from its intrinsic characteristics, power g
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Book chapters on the topic "Gray wolf optimizer"

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Gupta, Shubham, Kusum Deep, and Assif Assad. "Reliability–Redundancy Allocation Using Random Walk Gray Wolf Optimizer." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0035-0_75.

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Nabwy, Habiba Hassan, Shereen Mostafa Ali, Maira Mohamed Khafagy, et al. "Optimized Helical Antenna for Wireless Application at 2.4 GHz Using Gray Wolf Optimizer." In Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43247-7_38.

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Gupta, Indresh Kumar, Awanish Kumar Mishra, Shruti Patil, Rahul Mishra, and Joel J. P. C. Rodrigues. "Gray Wolf Optimizer with Machine Learning Enabled Diabetes Mellitus Recognition Model." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3244-2_41.

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Abualigah, Laith, Nada Khalil Al-Okbi, Seyedali Mirjalili, et al. "Moth-Flame Optimization Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and Sine Cosine Algorithm." In Handbook of Moth-Flame Optimization Algorithm. CRC Press, 2022. http://dx.doi.org/10.1201/9781003205326-16.

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Agrawal, Akhileshwar Prasad, and Nanhay Singh. "Reformed Binary Gray Wolf Optimizer (RbGWO) to Efficiently Detect Anomaly in IoT Network." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7346-8_21.

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Medjahed, Seyyid Ahmed, and Mohammed Ouali. "Spectral Band Selection Using Binary Gray Wolf Optimizer and Signal to Noise Ration Measure." In Modelling and Implementation of Complex Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05481-6_6.

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Ganguli, Souvik, Gagandeep Kaur, and Prasanta Sarkar. "A Hybrid Gray Wolf Optimizer for Modeling and Control of Permanent Magnet Synchronous Motor Drives." In Emerging Technologies in Data Mining and Information Security. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4052-1_72.

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Upendra, Rakesh Tripathi, and Tirath Prasad Sahu. "Binary Chaotic Gray Wolf Optimizer-Based Feature Selection for Intrusion Detection: A Comprehensive Study and Performance Evaluation." In Advances in Data-Driven Computing and Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9531-8_11.

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Okwu, Modestus O., and Lagouge K. Tartibu. "Grey Wolf Optimizer." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_5.

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Ali, Ahmed F., and Mohamed A. Tawhid. "Grey Wolf Optimizer." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-16.

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Conference papers on the topic "Gray wolf optimizer"

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Chen, Jun, Qiqi Shen, and Shanan Zhu. "Gray wolf optimizer based on gold rush optimizer." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039342.

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Gao, Yifan, Xinyu Li, Chunjiang Zhang, Zishun Hu, Yiping Gao, and Liang Gao. "An Improved Gray Wolf Optimizer for Wafer Probing Scheduling Problem." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033358.

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Chang, Chunguang, and Hui Wang. "Research on Prefabricated Component Production Scheduling Based on Improved Gray Wolf Optimizer." In Conference Proceedings of The 12th International Symposium on Project Management, China. Aussino Academic Publishing House (AAPH), 2024. http://dx.doi.org/10.52202/076061-0102.

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Chen, Yongli, Qian Zhang, Wanyuan Qing, Hongguang Dai, Youyang Wang, and Wenhua Gu. "Photovoltaic MPPT Algorithm Based on Improved Gray Wolf Optimizer for Partial Shading Conditions." In 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC). IEEE, 2024. https://doi.org/10.1109/eiecc64539.2024.10929512.

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Mustaqim, Tanzilal, Chastine Fatichah, Nanik Suciati, and Nathalya Dwi Kartika Sari. "Improving YOLOv8 Performance Using Hyperparameter Optimization with Gray Wolf Optimizer to Detect Acute Lymphoblastic Leukemia." In 2024 International Conference on Electrical and Information Technology (IEIT). IEEE, 2024. https://doi.org/10.1109/ieit64341.2024.10763126.

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Wang, Yufan, Yiming Sun, Jinliang Li, Jingsong Zheng, and Xiaowei Fu. "Multi-Strategy Hybrid Grey Wolf Optimizer." In 2024 International Conference on Cyber-Physical Social Intelligence (ICCSI). IEEE, 2024. https://doi.org/10.1109/iccsi62669.2024.10799327.

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Zhang, Zhaojun, Tao Xu, Kuansheng Zou, Simeng Tan, and Zhenzhen Sun. "Multi-Objective Grey Wolf Optimizer Based on Improved Head Wolf Selection Strategy." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662658.

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Madani, Hafian Fathul, Jondri, and Isman Kurniawan. "Implementation of Optimized Long Short-Term Memory with Grey Wolf Optimizer for Human Oral Bioavailability." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10651915.

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Jiang, Weiwei, and Min Peng. "Power Allocation with Grey Wolf Optimizer in Multibeam MANETs." In 2024 IEEE 7th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2024. https://doi.org/10.1109/auteee62881.2024.10869818.

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Makhadmeh, Sharif Naser, Wael Hadi, and Mohammed Azmi Al-Betar. "Grey Wolf Optimizer for Optimizing Renewable Energy Scheduling Problem." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013308.

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