Thematische Bibliographien / GWO

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Auswahl der wissenschaftlichen Literatur zum Thema „GWO“

Autor: Grafiati

Veröffentlicht am 16. Januar 2024

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "GWO"

Liu, Guangwei, Zhiqing Guo, Wei Liu, Feng Jiang, and Ensan Fu. "A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm." PLOS ONE 19, no. 1 (2024): e0295579. http://dx.doi.org/10.1371/journal.pone.0295579.

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This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the G

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Ikram, Misbah, Hongbo Liu, Ahmed Mohammed Sami Al-Janabi, et al. "Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm." Water 16, no. 21 (2024): 3038. http://dx.doi.org/10.3390/w16213038.

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For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMS

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Liu, Yuanyuan, Jiahui Sun, Haiye Yu, Yueyong Wang, and Xiaokang Zhou. "An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm." Applied Sciences 10, no. 18 (2020): 6343. http://dx.doi.org/10.3390/app10186343.

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Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithres

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Liu, Haiqiang, Gang Hua, Hongsheng Yin, and Yonggang Xu. "An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1723191.

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Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the g

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Widians, Joan Angelina, Retantyo Wardoyo, and Sri Hartati. "A Hybrid Ant Colony and Grey Wolf Optimization Algorithm for Exploitation-Exploration Balance." Emerging Science Journal 8, no. 4 (2024): 1642–54. http://dx.doi.org/10.28991/esj-2024-08-04-023.

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The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) are well-known nature-inspired algorithms. ACO is a metaheuristic search algorithm that takes inspiration from the behavior of real ants. In contrast, GWO is a grey wolf population-based heuristic algorithm. The important procedure in optimization is exploration and exploitation. ACO has excellent global and local search capabilities, and the exploration process is performed better than the exploitation process. In the case of regular, GWO is a greatly competitive algorithm compared to other common meta-heuristic algorithms, as it

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Zheng, Yukun, Ruyue Sun, Yixiang Liu, Yanhong Wang, Rui Song, and Yibin Li. "A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator." Actuators 12, no. 6 (2023): 220. http://dx.doi.org/10.3390/act12060220.

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Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-

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Zhao, Jiankun, Wei Liu, Qingxian Zhang, et al. "Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm." Journal of Instrumentation 17, no. 08 (2022): P08034. http://dx.doi.org/10.1088/1748-0221/17/08/p08034.

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Abstract Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into conside

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Sheng, Long, Sen Wu, and Zongyu Lv. "Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller." Applied Sciences 15, no. 8 (2025): 4530. https://doi.org/10.3390/app15084530.

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The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared

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Shi-fan, Qiao, Tan Jun-kun, Zhang Yong-gang, et al. "Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence." Advances in Civil Engineering 2021 (January 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/8896210.

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This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of ti

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Alsaeedi, Lec Ali Hakem, Suha Muhammed Hadi, Yarub Alazzawi, and Emad Badry Badry. "Eight-Figure Pattern for Enhancing the Searching Process of Grey Wolf Optimization (Eight-GWO)." Wasit Journal for Pure sciences 4, no. 2 (2025): 20–31. https://doi.org/10.31185/wjps.718.

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Evolutionary algorithms suffer significantly from a stack at the local optima. This paper proposes a new strategy that detects when the search gets stuck in a local optimum and then switches to a more dynamic approach to escape. The proposed model is based on simulating eight pattern movements and embedded with a Grey Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). The proposed model combines two phases: regular search when searching progresses over time while the second phase, searching by eight patterns when the algorithm reaches stuck. The Eigh

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Dissertationen zum Thema "GWO"

Biscaro, Michele <1996&gt. "Analisi Tecnica: ottimizzazione di un Trading System tramite PSO e GWO." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/21544.

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L’avvento di Internet e lo sviluppo della tecnologia hanno cambiato radicalmente il concetto di trading negli ultimi vent’anni. La figura stessa del trader è stata stravolta, tant’è che ad oggi, tra le competenze richieste, la conoscenza dei mercati finanziari non è più fondamentale quanto invece lo sono le abilità informatiche e matematiche. In questo contesto, l’Analisi Tecnica ha vissuto un periodo di rinascita e successo, sia per il fascino e la semplicità che la caratterizzano, sia per i notevoli progressi che lo sviluppo della tecnologia ha permesso di raggiungere. Oggi, numerosi ricerca

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Chan, Yick Mei Keriner. "The functions and occurrence patterns of aspect markers jo, gwo, gan, jyuh in Cantonese narrative discourse." HKBU Institutional Repository, 1999. http://repository.hkbu.edu.hk/etd_ra/401.

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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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Baldaccini, Chiara <1989&gt. "”Gao Jianli”, dramma storico “minore” di Guo Moruo: traduzione e commento." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4568.

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Attraverso la traduzione di "Gao Jianli", uno dei sei drammi storici di Guo Moruo, viene presentata quest'opera e proposta una ricerca sui motivi che hanno condotto l'autore ad impegnarsi nella stesura della stessa.

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Kirchhain, Simone. "Die Anwendung der Vertikal-GVO auf innerstaatliche Wettbewerbsbeschränkungen nach der 7. GWB-Novelle." Frankfurt,M. Berlin Bern Bruxelles New York, NY Oxford Wien Lang, 2006. http://d-nb.info/98596135X/04.

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Guo, Gao [Verfasser], K. D. [Gutachter] Wolff, M. [Gutachter] Farmand, and M. [Gutachter] Klein. "Retrospektive Untersuchung zu implantatverankerten Ohrepithesen / Gao Guo ; Gutachter: K. D. Wolff, M. Farmand, M. Klein." Berlin : Humboldt-Universität zu Berlin, 2005. http://d-nb.info/1207657913/34.

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Zheng, Ruiqin. "Chong gao yu tui fei : Wang Guowei, Lu Xun, Guo Moruo yu Yu Dafu = Sublime and decadence : Wang Guowei, Lu Xun, Guo Moruo and Yu Dafu /." click here to view the abstract and table of contents click here to view the fulltext, 2005. http://net3.hkbu.edu.hk/~libres/cgi-bin/thesisab.pl?pdf=b18843451a.pdf.

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Guo, Gao [Verfasser]. "Fibrillin-1 and elastin fragmentation in the pathogenesis of thoracic aortic aneurysm in Marfan syndrome / Gao Guo." Berlin : Freie Universität Berlin, 2011. http://d-nb.info/1026265851/34.

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Prudhomme, Jorian. "Phlébotomes et écosystèmes : impact des facteurs biotiques et abiotiques sur la structure génétique et phénotypique des populations." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS027/document.

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Les phlébotomes sont des insectes hématophages appartenant à la famille des Psychodidae et à la sous-famille des Phlebotominae. Cet insecte diptère, jaunâtre, relativement petit (2 à 3 mm) compte environ 800 espèces. 70 de ces espèces ont été identifiées comme vecteurs potentiels dont une quarantaine sont des vecteurs prouvés. Ils peuvent transmettre différents pathogènes dont les principaux sont les leishmanies et les phlébovirus. Ce travail de thèse est focalisé sur les phlébotomes vecteurs de la leishmaniose. Les leishmanioses sont des maladies parasitaires causées par un protozoaire du gen

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Gabriele, Marzia. "La circolazione delle ceramiche del Neolitico nel medio e alto Tirreno e nell’area ligure-provenzale : Studi di provenienza." Thesis, Nice, 2014. http://www.theses.fr/2014NICE2052.

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Les régions de la moyenne et haute Tyrrhénienne, la Ligurie et la Provence ont connu pendant le VIème millénaire BCE d’intenses activités maritimes et d’échange, dans le contexte de la néolithisation de la Méditerranée occidentale et intimement liées à l'exploitation et à la circulation des matières premières telles que l'obsidienne, le silex et les roches vertes; dans ce contexte, la définition de la circulation céramique constitue évidement un point central pour la compréhension des rapports économiques et culturels entre les différents groupes néolithiques.Afin de contribuer à la recherche

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Bücher zum Thema "GWO"

Tardieu, Gérard-Marie. Gwo koze: Nòt pou Responsab Twoup nou yo. Kopivit L'Action Sociale, 2009.

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Troupe, Georges. Méthode d'apprentissage des sept rythmes de Gwo Ka: Graphie et musique. I.G.E.S., 1988.

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Laumuno, Marie-Hélèna. Et le gwoka s'est enraciné en Guadeloupe: Chronologie d'un patrimoine culturel immatériel sensible. Nestor, 2012.

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Blou, Léna. Techni'ka: [Recherches sur l'émergence d'une méthode d'enseignement à partir des danses gwo-ka]. Jasor, 2005.

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ling, Zhang, ed. Gao sheng di guo. Zhong xin chu ban she, 2010.

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Changyun, Shen, ed. Zhao guo shi gao. Zhonghua shu ju, 2000.

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Guo, Shangbin. Guo Jijian su gao. Zhonghua shu ju, 1985.

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ming, Lian yu, and Wu jian zhong. 2007 zhong guo guo ce bao gao. Zhong guo shi dai jing ji chu ban she, 2007.

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Guo shi guan (China : Republic : 1949- ), ed. Guo shi ni zhuan: Zhonghua min guo guo shi gao. Guo shi guan, 1988.

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wen, Zhang you, and Huang ren wei. Zhong guo guo ji di wei bao gao. Ren min chu ban she, 2009.

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Buchteile zum Thema "GWO"

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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Ahsan, Fatma, and Faisal Anwer. "Hybrid GWO-PSO for Path Coverage Testing." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7371-8_16.

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Sahoo, Gopal Krishna, Niharika Patel, Debiprasad Panda, Shaswati Mishra, Sandeep Samantaray, and Deba Prakash Satapathy. "Streamflow Forecasting Using Novel ANFIS-GWO Approach." In Evolution in Computational Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7513-4_13.

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Shial, Gyanaranjan, Chitaranjan Tripathy, Sibarama Panigrahi, and Sabita Sahoo. "An Improved GWO Algorithm for Data Clustering." In Communications in Computer and Information Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21750-0_7.

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Nayak, Gayatri, Mitrabinda Ray, Swadhin Kumar Barisal, and Bichitrananda Patra. "GWO Based Test Sequence Generation and Prioritization." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9873-6_23.

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Ke, Jian, and Shiqian Yu. "Financial Crisis Prediction Based on GWO-SVM." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-222-4_58.

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Tripathi, Sandeep, Ashish Shrivastava, and K. C. Jana. "GWO Based PID Controller Optimization for Robotic Manipulator." In Intelligent Computing Techniques for Smart Energy Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0214-9_100.

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Han, Yue, Yuanjian Lv, and Zhifang Wang. "QR Code Digital Watermarking Algorithm Based on GWO." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8746-3_8.

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Jiang, Wei, Weiguo Zhang, and Jingping Shi. "UAV Path Planning Based on Improved GWO Algorithm." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_3.

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Fei, He, and Jiabei Shen. "Harmonic Elimination in Inverter Using Modified GWO Algorithm." In The proceedings of the 10th Frontier Academic Forum of Electrical Engineering (FAFEE2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3408-9_15.

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Konferenzberichte zum Thema "GWO"

Banerjee, Nabanita, Sumitra Mukhopadhyay, and Rajarshi Gupta. "AURG-GWO: Adaptive Updation and Random Grouping based GWO for Mental Stress Detection." In 2024 IEEE Calcutta Conference (CALCON). IEEE, 2024. https://doi.org/10.1109/calcon63337.2024.10914318.

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Kar, Manoj Kumar, and Chetan Sharma. "Load Frequency Controller Design using GWO Algorithm." In 2024 IEEE 4th International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC). IEEE, 2024. https://doi.org/10.1109/aespc63931.2024.10872128.

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Bhadoria, Utkarsh Singh, Prakash Narayan Tiwari, and Kishor Thakre. "Solar PV Parameter Extraction Using GWO-PSO Algorithm." In 2024 International Conference on Sustainable Power & Energy (ICSPE). IEEE, 2024. https://doi.org/10.1109/icspe62629.2024.10924344.

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Mishra, Alok Kumar, Jeevan Jyoti Mahakud, Manoj Kumar Debanath, Narayan Nahak, Debashisa Samal, and Akshaya Kumar Patra. "PQ Improvement with GWO Fuzzy PID Based SHAF." In 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON). IEEE, 2024. https://doi.org/10.1109/odicon62106.2024.10797585.

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Yu, Fan, Bingxin Tian, Hengquan Yu, and Jiuchun Ren. "An intrusion detection system based on GWO-CNN-BiLSTM." In Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), edited by Lvqing Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3038081.

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Chen, Yongqing, Yangyang Xia, Jiajia Chen, and Jie Chen. "Real-time Driving Ability Prediction Model Based on GWO-LSTM." In 2024 5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM). IEEE, 2024. http://dx.doi.org/10.1109/icmtim62047.2024.10629270.

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Mao, Xuwen, Junjie Wang, Wenbin Zhang, Jiawen Qian, and Haotian Qin. "Threat Assessment Method for Cluster Targets Based on GWO-IFE." In 2024 International Conference on Intelligent Computing and Robotics (ICICR). IEEE, 2024. http://dx.doi.org/10.1109/icicr61203.2024.00032.

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Song, Gang, Yingli Wan, Zhaoyu Ma, Anji Dong, and Shiyu Zhang. "Speed sensor interference source identification based on GWO-CFDP algorithm." In Workshop on Electronics Communication Engineering (WECE 2024), edited by Ning Xu. SPIE, 2025. https://doi.org/10.1117/12.3059166.

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Li, Chenggao, Haiping Ren, Juan Qin, and Meng Wang. "Speech signal denoising based on GWO-VMD combined with MODWT." In 2024 6th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP). IEEE, 2024. https://doi.org/10.1109/icmsp64464.2024.10866679.

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Yu, Mingshuang, Yun Zhu, Ziqin He, and Jianyu Wang. "Improvement of Multi-vehicle Queue Transformation Paths Based on MO-GWO." In 2024 International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2024. http://dx.doi.org/10.1109/icnsc62968.2024.10760185.

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Berichte der Organisationen zum Thema "GWO"

Hong, Weiwei, and Jean Parsons. Guo Poem. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1665.

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White, Sala, and Nikki Kujawa. Geo Queen. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1682.

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Johnston, W. R. GEO-GEO Cross-Calibration Results for AE9 Development. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada605733.

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Author, Unknown. GAO-06-945 GAO Natural Gas Pipeline Risk-Based Standards. Pipeline Research Council International, Inc. (PRCI), 2006. http://dx.doi.org/10.55274/r0012165.

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The Pipeline Safety Improvement Act of 2002 requires that operators (1) assess gas transmission pipeline segments in about 20,000 miles of highly populated or frequently used areas by 2012 for safety threats, such as incorrect operation and corrosion (called baseline assessments), (2) remedy defects, and (3) reassess these segments at least every 7 years. Under the Pipeline and Hazardous Materials Safety Administration's (PHMSA) regulations, operators must reassess their pipeline segments for corrosion at least every 7 years and for all safety threats at least every 10, 15, or 20 years, based

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Denli, Muzaffer. Genetiği değiştirilmiş organizmalar (GDO). İstanbul Ticaret Üniversitesi Kütüphane ve Dokümantasyon Daire Başkanlığı, 2010. http://dx.doi.org/10.63057/ticaretrapor.2024.68.

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Lewis, Joe, ed. Geo Video Web Service. Open Geospatial Consortium, Inc., 2006. http://dx.doi.org/10.62973/05-115.

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Opincaru, Cristian, ed. Trusted Geo Services IPR. Open Geospatial Consortium, Inc., 2007. http://dx.doi.org/10.62973/06-107r1.

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Junor, William. MAGI - Multi-Aperture GEO Imaging. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1072244.

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Hirsch, Leonard. Final Technical Report -- GEO-VI - USGEO. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1133797.

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Doveton, John H., and W. Lynn Watney. Geo-Engineering through Internet Informatics (GEMINI). Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/808530.

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