Relevant bibliographies by topics / Harris Hawks optimizer

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Academic literature on the topic 'Harris Hawks optimizer'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Harris Hawks optimizer"

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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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Milenković, Branislav, and Đorđe Jovanović. "The use of the biological algorithm in solving applied mechanics design problems." Scientific Technical Review 71, no. 1 (2021): 38–43. http://dx.doi.org/10.5937/str2101038m.

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Biologically inspired algorithms are becoming powerful in modern optimization. In this paper, the principles of a metaheuristic algorithm based on Harris hawks behavior are shown. The Harris Hawks Optimizer (HHO in short) was used for solving problems in applied mechanics (car side impact, cone clutch, three-dimensional beam and I beam optimization). In the end, a comparison of the results obtained by HHO and results obtained by other methods is given.
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Ibrahim, Mohammed K., Umi Kalsom Yusof, and Rosni Abdullah. "Harris Hawks Optimizer for Solving Multiple Sequence Alignment." Journal of Physics: Conference Series 1997, no. 1 (2021): 012008. http://dx.doi.org/10.1088/1742-6596/1997/1/012008.

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Mehta, Pranav, Betul Sultan Yildiz, Sadiq M. Sait, and Ali Riza Yildiz. "Hunger games search algorithm for global optimization of engineering design problems." Materials Testing 64, no. 4 (2022): 524–32. http://dx.doi.org/10.1515/mt-2022-0013.

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Abstract The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-à-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristic
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Recio-Colmenares, Roxana, Kelly Joel Gurubel-Tun, and Virgilio Zúñiga-Grajeda. "Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances." Applied Sciences 10, no. 20 (2020): 7073. http://dx.doi.org/10.3390/app10207073.

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In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Opti
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Jouhari, Hamza, Deming Lei, Mohammed A. A. Al-qaness, et al. "Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems." Symmetry 12, no. 9 (2020): 1460. http://dx.doi.org/10.3390/sym12091460.

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Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while
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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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Salam Abd Elminaam, Diaa, Nabil Neggaz, Ibrahim Abdulatief Ahmed, and Ahmed El Sawy Abouelyazed. "Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining." Computers, Materials & Continua 69, no. 3 (2021): 4129–49. http://dx.doi.org/10.32604/cmc.2021.019047.

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Singh, Tribhuvan. "A chaotic sequence-guided Harris hawks optimizer for data clustering." Neural Computing and Applications 32, no. 23 (2020): 17789–803. http://dx.doi.org/10.1007/s00521-020-04951-2.

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Zou, Lewang, Shihua Zhou, and Xiangjun Li. "An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection." Entropy 24, no. 8 (2022): 1065. http://dx.doi.org/10.3390/e24081065.

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To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We test
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Book chapters on the topic "Harris Hawks optimizer"

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Thaher, Thaer, Ali Asghar Heidari, Majdi Mafarja, Jin Song Dong, and Seyedali Mirjalili. "Binary Harris Hawks Optimizer for High-Dimensional, Low Sample Size Feature Selection." In Algorithms for Intelligent Systems. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9990-0_12.

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Liu, Xinyu, Yuxin Ma, and Peng Shao. "Multi-mission UAV Trajectory Planning in Smart Agriculture with Polarization Learning Model-Driven by Harris Hawks Optimizer." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3948-6_18.

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Deshmukh, Ankush R., and P. B. Ambhore. "Hybrid Bat Harris Hawks Optimized Approach for Data Retrieval Using Deep Convolution Neural Networks." In Multi-Strategy Learning Environment. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1488-9_22.

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Wei, Wei, and Chen Li. "Harris Hawk Optimized Interactive Multi-model Target Tracking Method Using Particle Filtering." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3948-6_26.

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Su, Hang, Yongbin Sun, and Haibin Duan. "Enhanced Harris Hawk Optimized Image Segmentation Model for Unmanned Aerial Vehicle Remote Sensing Scene Segmentation." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2224-5_15.

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Mallick, Nirmalya, Ayani Nandi, and Ranadip Roy. "Profit-Based Unit Commitment Using Local and Global Search Methods." In Robotics and Automation in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815223491124010014.

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The availability of clean energy is crucial for both the environment and human health. Numerous harmful gasses released by conventional automobiles cause illnesses and ailments in people all over the world. Nevertheless, there is growing interest in Plug-in Electric Vehicles (PEVs) to help with the energy and climate emergency. It has been noted that the manufacturing of PEVs has dramatically increased over the past ten years. The PEVs may supply the power grid with electricity while both consuming it and storing it in batteries. By effectively managing the electric demand profile and integrat
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Roy, Ranadip, Ayani Nandi, and Nirmalya Mallick. "Hybrid Optimization of Profit-Based Unit Commitment Allowing for Uncertainties of Renewable Energy Sources in Summer and Wintertime." In Robotics and Automation in Industry 4.0. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815223491124010006.

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Environmental issues, due to the various gases that are harmful to fossil fuels, can cause disease and sickness worldwide. Renewable energy sources (RESs) are a crucial solution for decreasing reliance on fossil fuels. This is because they offer several advantages, such as significant cost reductions in operations, minimal depreciation over time, and the ability to provide electric power for various applications. As a result, they are highly desirable for use in the power sector. This kind of trouble becomes excessively challenging by developing the extent of the electric power market step by
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Vonteddu, Shanmukha Naga Raju, Ravindra Kollu, and Prasanthi Kumari Nunna. "Design and Evaluation of Battery Management Systems for Electric Vehicle Applications With Harris Hawk Optimized Algorithm (HHOA) Based on Fuzzy Logic Controller." In Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6631-5.ch004.

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Utilizing electric vehicles (EVs) that don't pollute the atmosphere is one way to solve environmental pollution. This chapter as per the author proposes Harris Hawk optimized algorithm (HHOA) based on fuzzy logic controller with battery management system in electric vehicles. The suggested system is made to alter and regulate the current values of battery characteristics like battery voltage, current, power usage, and state of charge (SOC). To estimate the model temperature dependency, at various temperatures, the heat parameters can be found. Then, these temperature-dependent parameters are e
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Conference papers on the topic "Harris Hawks optimizer"

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Azarnik, Ahmad, and Arash Khosravi. "Optimized Intrusion Detection Using Harris Hawks Feature Selection and Deep Learning: A High-Performance Approach." In 2025 11th International Conference on Web Research (ICWR). IEEE, 2025. https://doi.org/10.1109/icwr65219.2025.11006257.

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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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Jeeva.S, Yuvaraj.T, and Ajoy Soban.M. "Integration of Biomass-Based RDGs for Power Loss Reduction in Radial Distribution Systems Using the Harris Hawk's Optimizer Algorithm." In 2024 10th International Conference on Electrical Energy Systems (ICEES). IEEE, 2024. https://doi.org/10.1109/icees61253.2024.10776882.

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Rong, Jingfeng, and Di Wang. "Harris Hawks Optimizer." In AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture. ACM, 2021. http://dx.doi.org/10.1145/3495018.3495048.

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Dhawale, Dinesh, and Vikram Kumar Kamboj. "hHHO-IGWO: A New Hybrid Harris Hawks Optimizer for Solving Global Optimization Problems." In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). IEEE, 2020. http://dx.doi.org/10.1109/iccakm46823.2020.9051509.

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Jiang, Yiheng. "Landfill settlement prediction study based on Harris Hawks Optimizer-Generalized Regression Neural Network." In 2024 5th International Conference on Computer Engineering and Application (ICCEA). IEEE, 2024. http://dx.doi.org/10.1109/iccea62105.2024.10603781.

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V, Vardumyan Arman, and Kostanyan Hakob T. "A Harris Hawks Optimizer Based Hybrid Algorithm for Optimal Design of Analog Integrated Circuits." In 2022 International Conference on Frontiers of Communications, Information System and Data Science (CISDS). IEEE, 2022. http://dx.doi.org/10.1109/cisds57597.2022.00031.

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Vikas, Ramesh Pandharinath Daund, Davinder Kumar, Piyush Charan, Ravi Shireesh Kumar Ingilela, and Ravi Rastogi. "Intrusion Detection in Wireless Sensor Networks using Hybrid Deep Belief Networks and Harris Hawks Optimizer." In 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2023. http://dx.doi.org/10.1109/icesc57686.2023.10193270.

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Kumar, G. Sanjay, S. N. V. S. K. Chaitanya, Venkateswara Rao, and R. Ashok Bakkiyaraj. "Optimal Reactive Power Dispatch solution accomplished with incorporation of Solar power using Harris Hawks Optimizer Algorithm." In 2022 4th International Conference on Energy, Power and Environment (ICEPE). IEEE, 2022. http://dx.doi.org/10.1109/icepe55035.2022.9798031.

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Tiwari, Rajeev Kumar, and S. Murugappan. "Enhancing Security in Cloud Computing and Protocols Using Harris Hawks Optimizer with Deep Learning for Intrusion Detection." In 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2023. http://dx.doi.org/10.1109/icscna58489.2023.10370663.

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