Relevant bibliographies by topics / Hawks algorithm

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  2. Dissertations / Theses
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Academic literature on the topic 'Hawks algorithm'

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

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Journal articles on the topic "Hawks algorithm"

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Jiao, Shangbin, Chen Wang, Rui Gao, Yuxing Li, and Qing Zhang. "Harris Hawks Optimization with Multi-Strategy Search and Application." Symmetry 13, no. 12 (2021): 2364. http://dx.doi.org/10.3390/sym13122364.

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The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harri
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XU, Xiaohan, Haima YANG, Heqing ZHENG, et al. "Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy." Wuhan University Journal of Natural Sciences 28, no. 6 (2023): 461–73. http://dx.doi.org/10.1051/wujns/2023286461.

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Because of the low convergence accuracy of the basic Harris Hawks algorithm, which quickly falls into the local optimal, a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy (TDHHO) is proposed. The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development. The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed. The difference variation strategy is used to mutate the individua
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Gezici, Harun, and Haydar Livatyalı. "Chaotic Harris hawks optimization algorithm." Journal of Computational Design and Engineering 9, no. 1 (2022): 216–45. http://dx.doi.org/10.1093/jcde/qwab082.

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Abstract Harris hawks optimization (HHO) is a population-based metaheuristic algorithm, inspired by the hunting strategy and cooperative behavior of Harris hawks. In this study, HHO is hybridized with 10 different chaotic maps to adjust its critical parameters. Hybridization is performed using four different methods. First, 15 test functions with unimodal and multimodal features are used for the analysis to determine the most successful chaotic map and the hybridization method. The results obtained reveal that chaotic maps increase the performance of HHO and show that the piecewise map method
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Liyi Zhang, Liyi Zhang, Zuochen Ren Liyi Zhang, Ting Liu Zuochen Ren, and Jinyan Tang Ting Liu. "Improved Artificial Bee Colony Algorithm Based on Harris Hawks Optimization." 網際網路技術學刊 23, no. 2 (2022): 379–89. http://dx.doi.org/10.53106/160792642022032302016.

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<p>Artificial bee colony algorithm, as a kind of bio-like intelligent algorithm, used by various optimization problems because of its few parameters and simple structure. However, there are also shortcomings such as low convergence accuracy, slow convergence speed, and not easy to jump out of the local optimum. Aiming at this shortcoming, this paper proposes an evolutionary algorithm of improved artificial bee colony algorithm based on reverse learning Harris Hawk (HABC). The basic inspiration of HABC comes from the good convergence of Harris Hawk algorithm in the process of finding the
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Hussien, Abdelazim G., Laith Abualigah, Raed Abu Zitar, et al. "Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications." Electronics 11, no. 12 (2022): 1919. http://dx.doi.org/10.3390/electronics11121919.

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other
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Xu, Jing, Chaofan Ren, and Xiaonan Chang. "Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm." Axioms 12, no. 3 (2023): 245. http://dx.doi.org/10.3390/axioms12030245.

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Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the i
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Cui-Cui Cai, Cui-Cui Cai, Mao-Sheng Fu Cui-Cui Cai, Xian-Meng Meng Mao-Sheng Fu, Qi-Jian Wang Xian-Meng Meng, and Yue-Qin Wang Qi-Jian Wang. "Modified Harris Hawks Optimization Algorithm with Multi-strategy for Global Optimization Problem." 電腦學刊 34, no. 6 (2023): 091–105. http://dx.doi.org/10.53106/199115992023123406007.

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<p>As a novel metaheuristic algorithm, the Harris Hawks Optimization (HHO) algorithm has excellent search capability. Similar to other metaheuristic algorithms, the HHO algorithm has low convergence accuracy and easily traps in local optimal when dealing with complex optimization problems. A modified Harris Hawks optimization (MHHO) algorithm with multiple strategies is presented to overcome this defect. First, chaotic mapping is used for population initialization to select an appropriate initiation position. Then, a novel nonlinear escape energy update strategy is presented to control t
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Iswisi, Amal F. A., Oğuz Karan, and Javad Rahebi. "Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm." BioMed Research International 2021 (December 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/3248834.

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The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this
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Hussien, Abdelazim G., Fatma A. Hashim, Raneem Qaddoura, Laith Abualigah, and Adrian Pop. "An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization." Processes 10, no. 11 (2022): 2254. http://dx.doi.org/10.3390/pr10112254.

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Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle algorithm (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in the sub-optimal region and have a slow convergence, especially in high-dimensional tasks problems. This paper suggests an enhanced ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as an internal operator in the updating process. ErWCA also uses a control-randomization operator. To verify this version, a comparison between EErWCA and other algorithms, namely, classical ErWCA, water cy
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Li, Xiaoyu. "An Improved Harris Hawks Optimization Algorithm for Solving the Permutation Flow Shop Scheduling Problem." Journal of Computing and Electronic Information Management 12, no. 3 (2024): 89–93. http://dx.doi.org/10.54097/q6hkkjlp.

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In this paper, an improved Harris Hawks optimization algorithm is proposed to solve the permutation flow shop scheduling problem with the objective of minimizing the completion time. Logistic chaotic mapping and inverse learning strategy are used to generate a high-quality initial population. A golden sine algorithm is introduced to improve the position update method. A nonlinear escape energy factor and adaptive t-distribution strategy are introduced to solve the problem of imbalance between the exploration and exploitation phases of the HHO algorithm. The effectiveness of the improved Harris
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Dissertations / Theses on the topic "Hawks algorithm"

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Tran, Long Quoc. "Efficient inference algorithms for network activities." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53499.

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The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals' activities in the social network. The inference of relationship between users/nodes or groups of users/nodes could be further complicated when activities are interval-censored, that is, when one only observed the number of activities that occurred in certain time windows. The same phenomenon happens in the online advertisement world whe
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Haghdan, Maysam. "Hawkes Process Models for Unsupervised Learning on Uncertain Event Data." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1503679661498849.

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Louzada, Pinto Julio Cesar. "Information diffusion and opinion dynamics in social networks." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0001/document.

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La dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds in
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Louzada, Pinto Julio Cesar. "Information diffusion and opinion dynamics in social networks." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0001.

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La dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds in
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Rambaldi, Marcello. "Some applications of Hawkes point processes to high frequency finance." Doctoral thesis, Scuola Normale Superiore, 2017. http://hdl.handle.net/11384/85718.

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Phi, Tien Cuong. "Décomposition de Kalikow pour des processus de comptage à intensité stochastique." Thesis, Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ4029.

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L'objectif de cette thèse est de construire des algorithmes capables de simuler l'activité d'un réseau de neurones. L'activité du réseau de neurones peut être modélisée par le train de spikes de chaque neurone, qui sont représentés par un processus ponctuel multivarié. La plupart des approches connues pour simuler des processus ponctuels rencontrent des difficultés lorsque le réseau sous-jacent est de grande taille.Dans cette thèse, nous proposons de nouveaux algorithmes utilisant un nouveau type de décomposition de Kalikow. En particulier, nous présentons un algorithme permettant de simuler l
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Books on the topic "Hawks algorithm"

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Sloot, Bart, and Aviva Groot, eds. The Handbook of Privacy Studies. Amsterdam University Press, 2018. http://dx.doi.org/10.5117/9789462988095.

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The Handbook of Privacy Studies is the first book in the world that brings together several disciplinary perspectives on privacy, such as the legal, ethical, medical, informatics and anthropological perspective. Privacy is in the news almost every day: mass surveillance by intelligence agencies, the use of social media data for commercial profit and political microtargeting, password hacks and identity theft, new data protection regimes, questionable reuse of medical data, and concerns about how algorithms shape the way we think and decide. This book offers interdisciplinary background informa
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McCandless, Luke. Tinder Code: Secret Hacks to Crack the Algorithm and Increase Your Matches 10x. Independently Published, 2019.

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Levy, Christopher. INSTAGRAM MARKETING for BUSINESS 2020 and YOUTUBE: Beginners Mastery Secrets on How Algorithms Work to Become Influencer and YouTuber-Preneur with a Vastly Followed Channel Exploiting Advertising Hacks. Independently Published, 2020.

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Levy, Christopher. Instagram Marketing for Business 2020: Beginners Secrets on How Algorithms Work to Become a Fruitful Influencer with Advertising Hacks, the Power of Stories ... More Followers More Potential Customers. Independently Published, 2020.

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Book chapters on the topic "Hawks algorithm"

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Zhang, Li-Gang, Xingsi Xue, and Shu-Chuan Chu. "Improving K-Means with Harris Hawks Optimization Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8048-9_10.

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Sharma, Ashutosh, Akash Saxena, Shalini Shekhawat, Rajesh Kumar, and Akhilesh Mathur. "Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm." In Bio-inspired Neurocomputing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5495-7_20.

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Sajid, Mohammad, Md Saquib Jawed, Shafiqul Abidin, Mohammad Shahid, Shakeel Ahamad, and Jagendra Singh. "Capacitated Vehicle Routing Problem Using Algebraic Harris Hawks Optimization Algorithm." In Intelligent Techniques for Cyber-Physical Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003438588-12.

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Kong, Li Sheng, Muhammed Basheer Jasser, Bayan Issa, Samuel-Soma M. Ajibade, Anwar P. P. Abdul Majeed, and Yang Luo. "An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3949-6_45.

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Turabieh, Hamza, and Majdi Mafarja. "Controlling Population Diversity of Harris Hawks Optimization Algorithm Using Self-adaptive Clustering Approach." In Evolutionary Data Clustering: Algorithms and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4191-3_7.

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Lei, Lin-yuan, Shu-Chuan Chu, Jian-po Li, Vaclav Snasel, and Jeng-Shyang Pan. "Research on Improvement of Harris Hawks Algorithm Based on Multi-strategy Optimization." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1750-0_34.

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Zivkovic, Miodrag, Timea Bezdan, Ivana Strumberger, Nebojsa Bacanin, and K. Venkatachalam. "Improved Harris Hawks Optimization Algorithm for Workflow Scheduling Challenge in Cloud–Edge Environment." In Computer Networks, Big Data and IoT. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0965-7_9.

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Ghosh, Moumita, B. Tudu, and K. K. Mandal. "Optimal Capacity and Location of DGs in Radial Distribution Network Using Novel Harris Hawks Optimization Algorithm." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1528-3_4.

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Verma, Sumit, Suprava Chakraborty, and Aprajita Salgotra. "Harris' Hawks Optimization Algorithm for Sizing and Allocation of Renewable Energy Based Distributed Generators." In Advanced Control & Optimization Paradigms for Energy System Operation and Management. River Publishers, 2023. http://dx.doi.org/10.1201/9781003337003-8.

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Laub, Patrick J., Young Lee, and Thomas Taimre. "EM Algorithm." In The Elements of Hawkes Processes. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84639-8_6.

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Conference papers on the topic "Hawks algorithm"

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Wei, Junyi, Hongbin Dong, and Xiaoping Zhang. "Information interconnection Harris hawks hybrid optimization algorithm." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822249.

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Zhi, Chaoyang. "Multi - Strategy Adaptive Harris Hawks Algorithm and its Applications." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035118.

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Abuelneel, Mahmoud, Ali M. El-Rifaie, Mayar Abdelaziz, Tamer Mohamed Barakat, Mokhtar Said, and Mohamed Barakat. "Optimizing Cascade Control for Load Frequency Management Using Harris Hawks Algorithm." In 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). IEEE, 2025. https://doi.org/10.1109/reepe63962.2025.10970978.

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Abualhaj, Mosleh M., Sumaya N. Al-Khatib, Ali Al-Allawee, Alhamza Munther, and Mohammed Anbar. "Enhancing Intrusion Detection Systems: A Comparative Study using Whale Optimization Algorithm and Hawks Optimization Algorithm." In 2024 11th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2024. https://doi.org/10.1109/iceee62185.2024.10779238.

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Xu, Yuyao, Lianglun Cheng, Tao Wang, and Mingzhe Ni. "An Improved Harris Hawks Optimization Algorithm for Microservice Composition and Collaborative Optimization." In 2024 29th International Conference on Automation and Computing (ICAC). IEEE, 2024. http://dx.doi.org/10.1109/icac61394.2024.10718842.

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Bento, Pedro M. R., Jose A. N. Pombo, Silvio J. P. S. Mariano, and Maria R. A. Calado. "A Modified Harris Hawks Optimization Algorithm for Solving the Optimal Power Flow Problem." In 2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2024. http://dx.doi.org/10.1109/eeeic/icpseurope61470.2024.10751622.

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Rajani, Koduri Naga, Ravikanth Thummala, Arun M, Venkata Syamala Raju Talari, N. Naga Saranya, and V. Gokula Krishnan. "Enhancement of Security in MANET using Modified Crow Harris Hawks Optimization Algorithm for IoT Applications." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10987940.

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Emambocus, Bibi Aamirah Shafaa, Muhammed Basheer Jasser, Shou Heng Tan, et al. "An Optimized Harris Hawks Algorithm for Enhancing ANN Performance in Prediction Tasks Applied in Sales Domain." In 2024 IEEE 14th International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2024. http://dx.doi.org/10.1109/iccsce61582.2024.10696513.

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An, Yang, Changsheng Zhang, and Jintao Shao. "An Enhanced Harris Hawks Optimization Algorithm for GRU Hyperparameter Tuning: Application to Mooring System Tension Prediction." In 2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI). IEEE, 2024. https://doi.org/10.1109/ricai64321.2024.10911481.

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Ramya, S., and K. Kalimuthu. "Convolutional Neural Network using Metaheuristic Improved Harris Hawks' Optimization Algorithm for Weed Classification in Cotton Crops." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859940.

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