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Journal articles on the topic 'Harris Hawks optimizer'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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9

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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Madiwa, Shweta M., and Vishwanath Burkpalli. "Sine Cosine Based Harris Hawks Optimizer: A Hybrid Optimization Algorithm for Skin Cancer Detection Using Deep Stack Auto Encoder." Revue d'Intelligence Artificielle 36, no. 5 (2022): 697–708. http://dx.doi.org/10.18280/ria.360506.

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Skin cancer is becoming major problems due to its tremendous growth. Skin cancer is a malignant skin lesion, which may cause damage to human. Hence, prior detection and precise medical diagnosis of the skin lesion is essential. In medical practice, detection of malignant lesions needs pathological examination and biopsy, which is expensive. The existing techniques need a brief physical inspection, which is imprecise and time-consuming. This paper presents a computer-assisted skin cancer detection strategy for detecting the skin lesion in skin images using deep stacked auto encoder. Sine Cosine

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Al-Wajih, Ranya, Said Jadid Abdulkadir, Norshakirah Aziz, Qasem Al-Tashi, and Noureen Talpur. "Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection." IEEE Access 9 (2021): 31662–77. http://dx.doi.org/10.1109/access.2021.3060096.

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Ren, Jia, Yikang Yang, Mingwei Sun, Zengqiang Chen, and Feng Duan. "Improved Harris Hawks Optimizer Tuned Compensation Function Observer for Ship Course." International Journal of Control, Automation and Systems 22, no. 12 (2024): 3801–11. https://doi.org/10.1007/s12555-023-0101-8.

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Kamboj, Vikram Kumar, Ayani Nandi, Ashutosh Bhadoria, and Shivani Sehgal. "An intensify Harris Hawks optimizer for numerical and engineering optimization problems." Applied Soft Computing 89 (April 2020): 106018. http://dx.doi.org/10.1016/j.asoc.2019.106018.

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Afreen, Sana, Ajay Kumar Bhurjee, and Rabia Musheer Aziz. "Gene selection with Game Shapley Harris hawks optimizer for cancer classification." Chemometrics and Intelligent Laboratory Systems 242 (November 2023): 104989. http://dx.doi.org/10.1016/j.chemolab.2023.104989.

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Abualhaj, Mosleh M., Ahmad Adel Abu-Shareha, Sumaya Nabil Alkhatib, Qusai Y. Shambour, and Adeeb M. Alsaaidah. "Detecting spam using Harris Hawks optimizer as a feature selection algorithm." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2361–69. https://doi.org/10.11591/eei.v14i3.9198.

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The Harris Hawks optimization (HHO) was used in this study to enhance spam identification. Only the features with a high influence on spam detection have been selected using the HHO metaheuristic technique. The HHO technique's assessment of the selected features was conducted using the ISCX-URL2016 dataset. The ISCX-URL2016 dataset has 72 features, but the HHO technique reduces that to just 10 features. Extra tree (ET), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques are used to complete the classification assignment. 99.81% accuracy is attained by the ET, 99.6

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Samantaray, Leena, Sabonam Hembram, and Rutuparna Panda. "A New Harris Hawks-Cuckoo Search Optimizer for Multilevel Thresholding of Thermogram Images." Revue d'Intelligence Artificielle 34, no. 5 (2020): 541–51. http://dx.doi.org/10.18280/ria.340503.

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The exploitation capability of the Harris Hawks optimization (HHO) is limited. This problem is solved here by incorporating features of Cuckoo search (CS). This paper proposes a new algorithm called Harris hawks-cuckoo search (HHO-CS) algorithm. The algorithm is validated using 23 Benchmark functions. A statistical analysis is carried out. Convergence of the proposed algorithm is studied. Nonetheless, converting color breast thermogram images into grayscale for segmentation is not effective. To overcome the problem, we suggest an RGB colour component based multilevel thresholding method for br

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Khan, Kaffayatullah, Mudassir Iqbal, Rahul Biswas, et al. "A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms." Polymers 14, no. 15 (2022): 3097. http://dx.doi.org/10.3390/polym14153097.

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The current work presents a comparative study of hybrid models that use support vector machines (SVMs) and meta-heuristic optimization algorithms (MOAs) to predict the ultimate interfacial bond strength (IBS) capacity of fiber-reinforced polymer (FRP). More precisely, a dataset containing 136 experimental tests was first collected from the available literature for the development of hybrid SVM models. Five MOAs, namely the particle swarm optimization, the grey wolf optimizer, the equilibrium optimizer, the Harris hawks optimization and the slime mold algorithm, were used; five hybrid SVMs were

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Hadeel, Tariq Ibrahim, Jalil Mazher Wamidh, and Mahmood Jassim Enas. "Modified Harris Hawks optimizer for feature selection and support vector machine kernels." Modified Harris Hawks optimizer for feature selection and support vector machine kernels 29, no. 2 (2023): 942–53. https://doi.org/10.11591/ijeecs.v29.i2.pp942-953.

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The support vector machine (SVM), one of the most effective learning algorithms, has many real-world applications. The kernel type and its parameters have a significant impact on the SVM algorithm's effectiveness and performance. In machine learning, choosing the feature subset is a crucial step, especially when working with high-dimensional data sets. These crucial criteria were treated independently in the majority of earlier studies. In this research, we suggest a hybrid strategy based on the Harris Hawk optimization (HHO) algorithm. HHO is one of the lately suggested metaheuristic algo

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Dehkordi, Amin Abdollahi, Ali Safaa Sadiq, Seyedali Mirjalili, and Kayhan Zrar Ghafoor. "Nonlinear-based Chaotic Harris Hawks Optimizer: Algorithm and Internet of Vehicles application." Applied Soft Computing 109 (September 2021): 107574. http://dx.doi.org/10.1016/j.asoc.2021.107574.

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21

Wang, Shuang, Heming Jia, Qingxin Liu, and Rong Zheng. "An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization." Mathematical Biosciences and Engineering 18, no. 6 (2021): 7076–109. http://dx.doi.org/10.3934/mbe.2021352.

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<abstract> <p>This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verif

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Tariq Ibrahim, Hadeel, Wamidh Jalil Mazher, and Enas Mahmood Jassim. "Modified Harris Hawks optimizer for feature selection and support vector machine kernels." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (2023): 942. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp942-953.

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<span lang="EN-US">The support vector machine (SVM), one of the most effective learning algorithms, has many real-world applications. The kernel type and its parameters have a significant impact on the SVM algorithm's effectiveness and performance. In machine learning, choosing the feature subset is a crucial step, especially when working with high-dimensional data sets. These crucial criteria were treated independently in the majority of earlier studies. In this research, we suggest a hybrid strategy based on the Harris Hawk optimization (HHO) algorithm. HHO is one of the lately suggest

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23

Yonbawi, Saud, Sultan Alahmari, T. Satyanarayana Murthy, et al. "Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture." Computer Systems Science and Engineering 46, no. 2 (2023): 1533–47. http://dx.doi.org/10.32604/csse.2023.036296.

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24

Almutairi, Saleh Ateeq. "DL-MDF-OH2: Optimized Deep Learning-Based Monkeypox Diagnostic Framework Using the Metaheuristic Harris Hawks Optimizer Algorithm." Electronics 11, no. 24 (2022): 4077. http://dx.doi.org/10.3390/electronics11244077.

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At the time the world is attempting to get over the damage caused by the COVID-19 spread, the monkeypox virus threatens to evolve into a global pandemic. Human monkeypox was first recognized in Africa and has recently emerged in 103 countries outside Africa. However, monkeypox diagnosis in an early stage is difficult because of the similarity between it, chickenpox, cowpox and measles. In some cases, computer-assisted detection of monkeypox lesions can be helpful for quick identification of suspected cases. Infected and uninfected cases have added to a growing dataset that is publicly accessib

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Edee, Kofi. "Augmented Harris Hawks Optimizer with Gradient-Based-like Optimization: Inverse Design of All-Dielectric Meta-Gratings." Biomimetics 8, no. 2 (2023): 179. http://dx.doi.org/10.3390/biomimetics8020179.

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In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking prey. The hunting strategy is divided into two phases: exploration and exploitation. However, the original HHO algorithm performs poorly in the exploitation phase and may get trapped and stagnate in a basin of local optima. To improve the algorithm, we propose pre-selecting better initial candidates obtained

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Yu, Jiangtao, Chang-Hwan Kim, and Sang-Bong Rhee. "The Comparison of Lately Proposed Harris Hawks Optimization and Jaya Optimization in Solving Directional Overcurrent Relays Coordination Problem." Complexity 2020 (January 22, 2020): 1–22. http://dx.doi.org/10.1155/2020/3807653.

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In this paper, a lately proposed Harris Hawks Optimizer (HHO) is used to solve the directional overcurrent relays (DOCRs) coordination problem. To the best of the authors’ knowledge, this is the first time HHO is being used in the DOCRs coordination problem. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks from different directions, based on the dynamic nature of scenarios and escaping patterns of the prey. To test its performances in solving the DOCRs coordination problem, it is adopted in 3-bus, 4-bus, 8-bus, and 9-bus systems, which are formulated b

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Habib, Habib Ur Rahman, Asad Waqar, Sharoze Sohail, et al. "Optimal Placement and Sizing Problem for Power Loss Minimization and Voltage Profile Improvement of Distribution Networks under Seasonal Loads Using Harris Hawks Optimizer." International Transactions on Electrical Energy Systems 2022 (June 30, 2022): 1–49. http://dx.doi.org/10.1155/2022/8640423.

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Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks’ intelligent behaviors during the chasing of the prey. Furthermore, the authors have inv

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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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Yousri, Dalia, Seyedali Mirjalili, J. A. Tenreiro Machado, Sudhakar Babu Thanikanti, Osama elbaksawi, and Ahmed Fathy. "Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling." Engineering Applications of Artificial Intelligence 100 (April 2021): 104193. http://dx.doi.org/10.1016/j.engappai.2021.104193.

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Liu, Guangshuai, Zuoxin Li, Si Sun, Yuzou Yang, Xurui Li, and Wenyu Yi. "An Efficient Improved Harris Hawks Optimizer and Its Application to Form Deviation-Zone Evaluation." Sensors 23, no. 13 (2023): 6046. http://dx.doi.org/10.3390/s23136046.

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Evaluation of the deviation zone based on discrete measured points is crucial for quality control in manufacturing and metrology. However, deviation-zone evaluation is a highly nonlinear problem that is difficult to solve using traditional numerical optimization methods. Swarm intelligence has many advantages in solving this problem: it produces gradient-free, high-quality solutions and is characterized by its ease of implementation. Therefore, this study applies an improved Harris hawks algorithm (HHO) to tackle the problem. The average fitness is applied to replace the random operator in the

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31

Wang, Shuang, Heming Jia, Laith Abualigah, Qingxin Liu, and Rong Zheng. "An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems." Processes 9, no. 9 (2021): 1551. http://dx.doi.org/10.3390/pr9091551.

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Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering the characteristics of these two algorithms, an improved hybrid AO and HHO combined with a nonlinear escaping energy parameter and random opposition-based learning strategy is proposed, namely IHAOHHO, to improve the searching performance in this paper. Firstly,

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Alghlayini, Saifeddin, Mohammed Azmi Al-Betar, and Mohamed Atef. "Enhancing Non-Invasive Blood Glucose Prediction from Photoplethysmography Signals via Heart Rate Variability-Based Features Selection Using Metaheuristic Algorithms." Algorithms 18, no. 2 (2025): 95. https://doi.org/10.3390/a18020095.

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Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) mode

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Arora, Krishan, Ashok Kumar, Vikram Kumar Kamboj, et al. "Optimization Methodologies and Testing on Standard Benchmark Functions of Load Frequency Control for Interconnected Multi Area Power System in Smart Grids." Mathematics 8, no. 6 (2020): 980. http://dx.doi.org/10.3390/math8060980.

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In the recent era, the need for modern smart grid system leads to the selection of optimized analysis and planning for power generation and management. Renewable sources like wind energy play a vital role to support the modern smart grid system. However, it requires a proper commitment for scheduling of generating units, which needs proper load frequency control and unit commitment problem. In this research area, a novel methodology has been suggested, named Harris hawks optimizer (HHO), to solve the frequency constraint issues. The suggested algorithm was tested and examined for several regul

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Hüsrevoğlu, Mustafa, Artur Janowski, and Ahmet Emin Karkınlı. "Optimizing Vehicle Placement in the Residual Spaces of Unmarked Parking Areas: A Comparative Study of Heuristic Methods." Applied Sciences 15, no. 12 (2025): 6416. https://doi.org/10.3390/app15126416.

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Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle Swarm Optimization, Artificial Bee Colony, Gray Wolf Optimizer, Harris Hawks Optimizer, Phasor Particle Swarm Optimization, Multi-Population Based Differential Evolution, and the Colony-Based Search Algorithm. The experiments were conducted in two different p

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admin, admin, Aisha H. Abdalla Hashim, Khalid M. Osman Saeed, and Mohammed M. Osman Mokhtar. "Two-Person Intuitionistic Neutrosophic Soft Games with Harris Hawks Optimizer based Tweets Classification on NLP Applications." International Journal of Neutrosophic Science 24, no. 1 (2024): 314–26. http://dx.doi.org/10.54216/ijns.240128.

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With the widespread usage of social media in our day-to-day lives, it becomes a platform for persons to express and share their feelings, views, thoughts, and opinions. Recognizing emotions has numerous applications extending from dynamic advertisement to behavior analyses. People express their emotional state in a language that is often complemented by figures of speech and ambiguity, making it problematic even for human beings to understand. Categorizing tweets is a dynamic application of NLP, allowing the scrutiny of topical discussions, user opinions, and trends in real-time. Leveraging te

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Seçkiner, Serap Ulusam, and Şeyma Yilkici Yüzügüldü. "A new health-based metaheuristic algorithm: cholesterol algorithm." International Journal of Industrial Optimization 4, no. 2 (2023): 115–30. http://dx.doi.org/10.12928/ijio.v4i2.7651.

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This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Se

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Long, Wen, Jianjun Jiao, Ming Xu, Mingzhu Tang, Tiebin Wu, and Shaohong Cai. "Lens-imaging learning Harris hawks optimizer for global optimization and its application to feature selection." Expert Systems with Applications 202 (September 2022): 117255. http://dx.doi.org/10.1016/j.eswa.2022.117255.

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Long, Wen, Jianjun Jiao, Ming Xu, Mingzhu Tang, Tiebin Wu, and Shaohong Cai. "Lens-imaging learning Harris hawks optimizer for global optimization and its application to feature selection." Expert Systems with Applications 202 (September 2022): 117255. http://dx.doi.org/10.1016/j.eswa.2022.117255.

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Wei, Yan, Huijing Lv, Mengxiang Chen, et al. "Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer." IEEE Access 8 (2020): 76841–55. http://dx.doi.org/10.1109/access.2020.2982796.

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Tikhamarine, Yazid, Doudja Souag-Gamane, Ali Najah Ahmed, et al. "Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization." Journal of Hydrology 589 (October 2020): 125133. http://dx.doi.org/10.1016/j.jhydrol.2020.125133.

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Ridha, Hussein Mohammed, Ali Asghar Heidari, Mingjing Wang, and Huiling Chen. "Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models." Energy Conversion and Management 209 (April 2020): 112660. http://dx.doi.org/10.1016/j.enconman.2020.112660.

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Taha, Altyeb. "A novel deep learning-based hybrid Harris hawks with sine cosine approach for credit card fraud detection." AIMS Mathematics 8, no. 10 (2023): 23200–23217. http://dx.doi.org/10.3934/math.20231180.

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<abstract> <p>Credit cards have become an integral part of the modern financial landscape, and their use is essential for individuals and businesses. This has resulted in a significant increase in their usage in recent years, especially with the growing popularity of online payments. Unfortunately, this increase in credit card use has also led to a corresponding rise in credit card fraud, posing a serious threat to financial security and privacy. Therefore, this research introduces a novel deep learning-based hybrid Harris hawks with sine cosine method for credit card fraud detecti

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L. Lakshmaiah, Dr. K Raja, Dr. B. Rama Subba Reddy. "Gaussian Harris Hawks Optimizer (GHHO) Based Cluster Head Selection (CHS) And Enhanced Energy Harvesting Clustering (EEHC) Protocol for WSN." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 4518–31. http://dx.doi.org/10.52783/tjjpt.v44.i4.1736.

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Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped

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Chantar, Hamouda, Thaer Thaher, Hamza Turabieh, Majdi Mafarja, and Alaa Sheta. "BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems." Applied Sciences 11, no. 14 (2021): 6516. http://dx.doi.org/10.3390/app11146516.

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Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen

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Elaziz, Mohamed Abd, Ali Asghar Heidari, Hamido Fujita, and Hossein Moayedi. "A competitive chain-based Harris Hawks Optimizer for global optimization and multi-level image thresholding problems." Applied Soft Computing 95 (October 2020): 106347. http://dx.doi.org/10.1016/j.asoc.2020.106347.

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Yildiz, Ali Riza, and Pranav Mehta. "Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components." Materials Testing 64, no. 5 (2022): 706–13. http://dx.doi.org/10.1515/mt-2022-0012.

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Abstract The adaptability of metaheuristics is proliferating rapidly for optimizing engineering designs and structures. The imperative need for the fuel-efficient design of vehicles with lightweight structures is also a soaring demand raised by the different industries. This research contributes to both areas by using both the hybrid Taguchi salp swarm algorithm-Nelder–Mead (HTSSA-NM) and the manta ray foraging optimization (MRFO) algorithm to optimize the structure and shape of the automobile brake pedal. The results of HTSSA-NM and MRFO are compared with some well-established metaheuristics

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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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Houssein, Essam H., Nagwan Abdel Samee, Maali Alabdulhafith, and Mokhtar Said. "Extraction of PEM fuel cell parameters using Walrus Optimizer." AIMS Mathematics 9, no. 5 (2024): 12726–50. http://dx.doi.org/10.3934/math.2024622.

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<abstract> <p>The process of identifying the optimal unknown variables for the creation of a precision fuel-cell performance forecasting model using optimization techniques is known as parameter identification of the proton exchange membrane fuel cell (PEMFC). Recognizing these factors is crucial for accurately forecasting and assessing the fuel cell's performance, as they may not always be included in the manufacturer's datasheet. Six optimization algorithms—the Walrus Optimizer (WO), the Tunicate Swarm Algorithm (TSA), the Harris Hawks Optimizer (HHO), the Heap Based Optimizer (H

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Qiao, Hanqing, Minghe Zhang, and Maksim Bano. "Harris Hawks Optimization for Soil Water Content Estimation in Ground-Penetrating Radar Waveform Inversion." Remote Sensing 17, no. 8 (2025): 1436. https://doi.org/10.3390/rs17081436.

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Ground-penetrating radar (GPR) has emerged as a promising technology for estimating the soil water content (SWC) in the vadose zone. However, most current studies focus on partial GPR data, such as travel-time or amplitude, to achieve SWC estimation. Full waveform inversion (FWI) can produce more accurate results than inversion based solely on travel-time. However, it is subject to local minima when using a local optimization algorithm. In this paper, we propose a novel and powerful GPR waveform inversion scheme based on Harris hawks optimization (HHO) algorithm. The proposed strategy is teste

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Taha, Ashraf A., Hagar O. Abouroumia, Shimaa A. Mohamed, and Lamiaa A. Amar. "Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm." Future Internet 14, no. 12 (2022): 365. http://dx.doi.org/10.3390/fi14120365.

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As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enha

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