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Relevant bibliographies by topics / Binary grey wolf optimization

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Academic literature on the topic 'Binary grey wolf optimization'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Binary grey wolf optimization"

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Momanyi, Enock, and Davies Segera. "A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification." BioMed Research International 2021 (October 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/5556941.

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A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle

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Azami, Pegah, and Kalpdrum Passi. "Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms." Algorithms 17, no. 10 (2024): 425. http://dx.doi.org/10.3390/a17100425.

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In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each acc

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Khaseeb, Jomana Yousef, Arabi Keshk, and Anas Youssef. "Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization." Applied Sciences 15, no. 2 (2025): 489. https://doi.org/10.3390/app15020489.

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Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach mani

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Emary, E., Hossam M. Zawbaa, and Aboul Ella Hassanien. "Binary grey wolf optimization approaches for feature selection." Neurocomputing 172 (January 2016): 371–81. http://dx.doi.org/10.1016/j.neucom.2015.06.083.

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Al-Tashi, Qasem, Said Jadid Abdul Kadir, Helmi Md Rais, Seyedali Mirjalili, and Hitham Alhussian. "Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection." IEEE Access 7 (2019): 39496–508. http://dx.doi.org/10.1109/access.2019.2906757.

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Marot, Julien, Flora Zidane, Maha El-Abed, Jerome Lanteri, Jean-Yves Dauvignac, and Claire Migliaccio. "GWO-Based Joint Optimization of Millimeter-Wave System and Multilayer Perceptron for Archaeological Application." Sensors 24, no. 9 (2024): 2749. http://dx.doi.org/10.3390/s24092749.

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Recently, low THz radar-based measurement and classification for archaeology emerged as a new imaging modality. In this paper, we investigate the classification of pottery shards, a key enabler to understand how the agriculture was introduced from the Fertile Crescent to Europe. Our purpose is to jointly design the measuring radar system and the classification neural network, seeking the maximal compactness and the minimal cost, both directly related to the number of sensors. We aim to select the least possible number of sensors and place them adequately, while minimizing the false recognition

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Liu, Junxiu, Tiening Sun, Yuling Luo, Su Yang, Yi Cao, and Jia Zhai. "Echo state network optimization using binary grey wolf algorithm." Neurocomputing 385 (April 2020): 310–18. http://dx.doi.org/10.1016/j.neucom.2019.12.069.

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Zou, Jincheng, Junjun Chen, Weike Liu, Yujing Hu, Jun Wang, and Yi Xie. "Edge Detection Algorithm for Infrared Images of Power Equipment Based on Improved Grey Wolf Algorithm and Corrosion Algorithm." Journal of Physics: Conference Series 2666, no. 1 (2023): 012043. http://dx.doi.org/10.1088/1742-6596/2666/1/012043.

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Abstract The enhanced grey wolf algorithm is merged with the corrosion algorithm, and a grey wolf corrosion optimization algorithm is proposed to precisely identify the boundaries within infrared images of power equipment. The refined grey wolf algorithm employs exponential entropy as its fitness function and incorporates an individual wolf selection mechanism, enabling binary segmentation with adaptively chosen thresholds and high resemblance to the original image. Simulation results of actual infrared images captured in substations reveal that, compared to conventional edge detection algorit

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Too, Jingwei, Abdul Abdullah, Norhashimah Mohd Saad, Nursabillilah Mohd Ali, and Weihown Tee. "A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification." Computers 7, no. 4 (2018): 58. http://dx.doi.org/10.3390/computers7040058.

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Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT c

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Al-Moalmi, Ammar, Juan Luo, Ahmad Salah, and Kenli Li. "Optimal Virtual Machine Placement Based on Grey Wolf Optimization." Electronics 8, no. 3 (2019): 283. http://dx.doi.org/10.3390/electronics8030283.

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Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) techn

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Dissertations / Theses on the topic "Binary grey wolf optimization"

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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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Oliveira, Clemar Trentin. "Otimização de um trocador de calor casco e tubos utilizando o algoritmo lobo cinzento." Universidade do Vale do Rio dos Sinos, 2018. http://www.repositorio.jesuita.org.br/handle/UNISINOS/7648.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2019-03-13T13:59:33Z No. of bitstreams: 1 Clemar Trentin Oliveira_.pdf: 4154239 bytes, checksum: 0abf1d28fa69ea97d15e72a7624615f5 (MD5)<br>Made available in DSpace on 2019-03-13T13:59:33Z (GMT). No. of bitstreams: 1 Clemar Trentin Oliveira_.pdf: 4154239 bytes, checksum: 0abf1d28fa69ea97d15e72a7624615f5 (MD5) Previous issue date: 2018-12-11<br>Nenhuma<br>Neste trabalho, desenvolve-se uma nova abordagem de otimização do projeto de um trocador de calor casco e tubos. O algoritmo Otimizador por Lobo Cinzento (GWO) é aplicado para minimiz

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Elahi, Behin. "Integrated Optimization Models and Strategies for Green Supply Chain Planning." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1467266039.

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El-Bidairi, KSN. "Fuzzy-grey wolf optimization for energy storage sizing and power management in microgrids." Thesis, 2019. https://eprints.utas.edu.au/34066/1/El_Bidairi__whole_thesis.pdf.

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Escalating fossil fuel prices, pressure of more stringent environmental regulations, and deregulation in the electricity market provide opportunities and motives for renewable energy sources (RESs), such as wind, solar, tidal and wave energy to be integrated into existing power grids. Nevertheless, due to the distributed nature of RESs, the traditional centralized power system architecture, as well as control mechanisms, are not adequate to support the integration of renewable energy systems. Therefore, the concept of a microgrid emerged, where a group of distributed energy sources and loads,

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Chang, Pei-Cheng, and 張沛承. "A Modified Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/55p3tc.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>107<br>Partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a multiple-peak power-voltage curve. The common MHAs, such as particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO) and cuckoo search (CS) have been used for maximum power point tracking (MPPT). Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been u

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TRINH, BUI VAN, and BUI VAN TRINH. "A Hybrid Grey Wolf Optimization Algorithm using Robust Learning Mechanism for Large Scale Economic Load Dispatch with Valve-Point Effects." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/74cr6r.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>107<br>This research proposed a new hybrid algorithm of grey wolf optimization (GWO) integrated with robust learning mechanism to solve the large scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitate the hunting behavior and social hierarchy of grey wolves in nature and reinforced by robust tolerant based adjust searching direction and opposite based learning. This technique could effectively prevent search agents trapping into local optimum but also generate potential candidate to obtain feasible solutions.

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Book chapters on the topic "Binary grey wolf optimization"

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Jiang, Yongqi, Chu Jin, Quan Zhang, Biao Hu, and Zhenzhou Tang. "A Binary Multi-objective Grey Wolf Optimization for Feature Selection." In Knowledge Science, Engineering and Management. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5495-3_30.

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Moturi, Sireesha, Srikanth Vemuru, S. N. Tirumala Rao, and Sneha Ananya Mallipeddi. "Hybrid Binary Dragonfly Algorithm with Grey Wolf Optimization for Feature Selection." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3315-0_47.

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Yao, Zhencheng, Naifeng Liang, and Liudan Zhu. "Intelligent Logistics Automated Distribution Path Under Binary Grey Wolf Optimization Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2794-3_40.

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Li, Wenqi, Hui Kang, Tie Feng, Jiahui Li, Zhiru Yue, and Geng Sun. "Swarm Intelligence-Based Feature Selection: An Improved Binary Grey Wolf Optimization Method." In Knowledge Science, Engineering and Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82153-1_9.

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Mishra, Krishn Kumar. "Grey Wolf Optimization." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-6.

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Badar, Altaf Q. H. "Grey Wolf Optimizer." In Evolutionary Optimization Algorithms. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-8.

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Rebello, Gustavo, and Edimar José de Oliveira. "Modified Binary Grey Wolf Optimizer." In Springer Tracts in Nature-Inspired Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2133-1_7.

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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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Rezaei, Hossein, Omid Bozorg-Haddad, and Xuefeng Chu. "Grey Wolf Optimization (GWO) Algorithm." In Advanced Optimization by Nature-Inspired Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_9.

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Mirjalili, Seyedali, and Jin Song Dong. "Multi-objective Grey Wolf Optimizer." In Multi-Objective Optimization using Artificial Intelligence Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24835-2_5.

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Conference papers on the topic "Binary grey wolf optimization"

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R, Rajalaxmi R., Gothai E, Saraa R, and Nikitha S. "Feature Selection Using Binary Grey Wolf Optimization for Survival Prediction of Hepatocellular Carcinoma." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823297.

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S, Sivsakthiselvan, M. Palanivelan, Purushothaman K. E, Mohanraj S, and Asokan V. "Enhanced Energy-Efficient Routing in WSNs: A Multipath Approach Using Binary Gray Wolf Optimization and Sugeno Fuzzy Logic." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894592.

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Kumar, Santosh, Jyoti Bisht, and Tarun Sarawgi. "Multi-Criteria Adaptive Websites using Grey Wolf Optimization." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717589.

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Gupta, Megha, Abhinav Tomar, and Vibhor Kant. "Leveraging Grey Wolf Optimization for Multi-Criteria Recommender Systems." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752214.

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Lou, Tai-Shan, Haowei Fan, Zhendong He, and Guoqiang Ding. "Improved Grey Wolf Optimization Algorithm for UAV Path Planning." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865352.

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Abu-Shareha, Ahmad Adel, Mosleh M. Abualhaj, Ali AL-ALLAWEE, Alhamza Munther, and Mohammed Anbar. "Enhancing Malware Detection with Firefly and Grey Wolf Optimization Algorithms." In 2024 11th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2024. https://doi.org/10.1109/iceee62185.2024.10779310.

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Kori, Gururaj S., Mahabaleshwar S. Kakkasageri, Vijaykumar Hiremath, Poornima M. Chanal, Rajani S. Pujar, and Vinayak A. Telsang. "Cognitive Swarm Drones Attack Model: A Grey Wolf Optimization Approach." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677076.

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Yulistiani, Risma, and Felix Indra Kurniadi. "Feature Selection Using Grey Wolf Optimization for Fetal Health Classification." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10750950.

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Abdulsattar, Nejood F., Hassan Mohammed Abed, Amit Gangopadhyay, Mohammed I. Habelalmateen, Fatima Hashim Abbas, and Rusul Lsmael Hadi. "Energy Consumption Modeling and Grey Wolf Optimization for Vehicular Communication." In 2024 Asian Conference on Communication and Networks (ASIANComNet). IEEE, 2024. https://doi.org/10.1109/asiancomnet63184.2024.10811028.

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Sirhan, Najem, and Manel Martinez-Ramon. "Optimization of Multi-Hop Wireless Routing with Grey Wolf Optimizer." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013180.

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