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Journal articles on the topic 'Hawks algorithm'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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2

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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9

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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11

Reda, Mohamed, Ahmed Onsy, Amira Y. Haikal, and Ali Ghanbari. "Optimizing the Steering of Driverless Personal Mobility Pods with a Novel Differential Harris Hawks Optimization Algorithm (DHHO) and Encoder Modeling." Sensors 24, no. 14 (2024): 4650. http://dx.doi.org/10.3390/s24144650.

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This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent

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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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Milenković, Branislav. "Implementation of Harris Hawks Optimization (HHO) algorithm to solve engineering problems." Tehnika 76, no. 4 (2021): 439–46. http://dx.doi.org/10.5937/tehnika2104439m.

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Recently, optimization techniques have become very important and popular in different engineering applications. In this paper we demonstrate how Harris Hawks Optimization (HHO) algorithm can be used to solve certain optimization problems in engineering. In the second part, biological fundamentals, as well as method explanation are given. Afterwards, the HHO algorithm and its' applicability is explained in detail. The pseudo code for this algorithm was written using MATLAB R2019a software suite. Harris Hawks Optimization (HHO) algorithm was used for optimization of engineering problems, such as

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14

Zou, Tingting, and Changyu Wang. "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization." Mathematics 10, no. 7 (2022): 1145. http://dx.doi.org/10.3390/math10071145.

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The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the AR

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15

Dokeroglu, Tansel. "A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients." PeerJ Computer Science 9 (June 14, 2023): e1430. http://dx.doi.org/10.7717/peerj-cs.1430.

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Harris’ Hawk Optimization (HHO) is a novel metaheuristic inspired by the collective hunting behaviors of hawks. This technique employs the flight patterns of hawks to produce (near)-optimal solutions, enhanced with feature selection, for challenging classification problems. In this study, we propose a new parallel multi-objective HHO algorithm for predicting the mortality risk of COVID-19 patients based on their symptoms. There are two objectives in this optimization problem: to reduce the number of features while increasing the accuracy of the predictions. We conduct comprehensive experiments

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Nourbakhsh, Azamossadat, Mohammad Ordouei, and Bahareh Jalali. "Proposing a New Framework for Optimizing Energy Consumption in Sensor Nodes Used in the Internet of Things." Power System Technology 48, no. 1 (2024): 1686–705. https://doi.org/10.5281/zenodo.13821529.

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This  paper  presents  a  comparative  analysis  of  evolutionary  algorithms,  including genetic  algorithms,  particle  swarm  optimization,  cuckoo  algorithm,  and  the  Harris  Hawk Optimization algorithm, for optimizing vehicle routing in smart cities. The study evaluates the performance of these algorithms in minimizing costs and maximizing efficiency in the context of  providing  services  to  requesters.  Results  indicate  the  effectiveness  of

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Almotairi, Sultan, Elsayed Badr, Mustafa Abdul Salam, and Alshimaa Dawood. "Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization." Mathematics 11, no. 19 (2023): 4181. http://dx.doi.org/10.3390/math11194181.

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Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic m

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Jain, Vidhi. "Nature-inspired approaches in Software Fault Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34235.

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In software engineering, predicting software faults is a crucial task for ensuring high software quality and reducing costs. In recent years, nature inspired approaches have been increasingly used in software fault prediction. In this paper, we explore the effectiveness of six nature inspired algorithms, namely Ant Colony, Particle Swarm Optimization, Firefly, Bat, Harris Hawks, and Genetic Algorithm, for software fault prediction. We evaluate the algorithms using three commonly used datasets, JM1, CM1, and PC1. Our experimental results show that nature inspired approaches can effectively pred

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19

Wang, Haosen, Jun Tang, and Qingtao Pan. "MSI-HHO: Multi-Strategy Improved HHO Algorithm for Global Optimization." Mathematics 12, no. 3 (2024): 415. http://dx.doi.org/10.3390/math12030415.

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The Harris Hawks Optimization algorithm (HHO) is a sophisticated metaheuristic technique that draws inspiration from the hunting process of Harris hawks, which has gained attention in recent years. However, despite its promising features, the algorithm exhibits certain limitations, including the tendency to converge to local optima and a relatively slow convergence speed. In this paper, we propose the multi-strategy improved HHO algorithm (MSI-HHO) as an enhancement to the standard HHO algorithm, which adopts three strategies to improve its performance, namely, inverted S-shaped escape energy,

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Yasear, Shaymah Akram, and Ku Ruhana Ku-Mahamud. "Non-dominated sorting Harris’s hawk multi-objective optimizer based on reference point approach." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (2019): 1603. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1603-1614.

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A non-dominated sorting Harris’s hawk multi-objective optimizer (NDSHHMO) algorithm is presented in this paper. The algorithm is able to improve the population diversity, convergence of non-dominated solutions toward the Pareto front, and prevent the population from trapping into local optimal. This was achieved by integrating fast non-dominated sorting with the original Harris’s hawk multi-objective optimizer (HHMO). Non-dominated sorting divides the objective space into levels based on fitness values and then selects non-dominated solutions to produce the next generation of hawks. A set of w

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Heidari, Ali Asghar, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Huiling Chen. "Harris hawks optimization: Algorithm and applications." Future Generation Computer Systems 97 (August 2019): 849–72. http://dx.doi.org/10.1016/j.future.2019.02.028.

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Z. Almutair, Sulaiman, Hegazy Rezk, and Yahia Bahaa Hassan. "Robust parameter determination approach based on red-tailed hawk optimization used for lithium-ion battery." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3729. http://dx.doi.org/10.11591/ijece.v14i4.pp3729-3738.

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Lithium-ion electrochemical batteries are being used more in a large number of applications, such as electric vehicles. However, increasing their efficiency lies in the accuracy of their model. For this, extracting the best values of parameters of the battery model is needed. A recent metaheuristic optimizer named the red-tail hawk (RTH) is used in the current research to extract the battery parameters. The idea of this algorithm is extracted from hunting techniques of red-tail hawks. The RTH algorithm is more likely to avoid entangled local optimums because of its high diversity, fast converg

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Huang, Lin, Qiang Fu, and Nan Tong. "An Improved Harris Hawks Optimization Algorithm and Its Application in Grid Map Path Planning." Biomimetics 8, no. 5 (2023): 428. http://dx.doi.org/10.3390/biomimetics8050428.

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Aimed at the problems of the Harris Hawks Optimization (HHO) algorithm, including the non-origin symmetric interval update position out-of-bounds rate, low search efficiency, slow convergence speed, and low precision, an Improved Harris Hawks Optimization (IHHO) algorithm is proposed. In this algorithm, a circle map was added to replace the pseudo-random initial population, and the population boundary number was reduced to improve the efficiency of the location update. By introducing a random-oriented strategy, the information exchange between populations was increased and the out-of-bounds po

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Wen, Lei, Guopeng Wang, Longwang Yue, Xiaodan Liang, and Hanning Chen. "Multistrategy Harris Hawks Optimization Algorithm Using Chaotic Method, Cauchy Mutation, and Elite Individual Guidance." Discrete Dynamics in Nature and Society 2022 (August 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/5129098.

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Aiming at the shortcomings of the Harris hawks optimization algorithm (HHO), such as poor initial population diversity, slow convergence speed, poor local optimization ability, and easily falling into local optimum, a Harris hawks optimization algorithm (CCCHHO) integrating multiple mechanisms is proposed. First, the population diversity is enhanced by the initialization of the chaotic method. Second, the cosine function is used to better simulate the characteristics of the periodic change of the energy of the prey in the repeated contests with the group of hawks, to better balance the explora

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Z., Almutair Sulaiman, Hegazy Rezk, and Hassan Yahia Bahaa. "Robust parameter determination approach based on red-tailed hawk optimization used for lithium-ion battery." Robust parameter determination approach based on red-tailed hawk optimization used for lithium-ion battery 14, no. 4 (2024): 3729–38. https://doi.org/10.11591/ijece.v14i4.pp3729-3738.

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Lithium-ion electrochemical batteries are being used more in a large number of applications, such as electric vehicles. However, increasing their efficiency lies in the accuracy of their model. For this, extracting the best values of parameters of the battery model is needed. A recent metaheuristic optimizer named the red-tail hawk (RTH) is used in the current research to extract the battery parameters. The idea of this algorithm is extracted from hunting techniques of red-tail hawks. The RTH algorithm is more likely to avoid entangled local optimums because

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Ali, Ehab S. "Reconfiguration of a Radial Distribution System using the Harris Hawks Optimization Algorithm." WSEAS TRANSACTIONS ON SYSTEMS 24 (April 7, 2025): 156–63. https://doi.org/10.37394/23202.2025.24.17.

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Recently, reducing power loss in distribution systems has become a key focus of various studies due to its influence on gross costs and voltage gradients. One solution is the optimal reconfiguration of the Radial Distribution System (RDS). This study presents an inventive tactic to reconfigure RDS by selecting the best switch combinations while considering system operating constraints, using the Harris Hawks Approach (HHA) which is a nature-inspired optimization paradigm. The primary inspiration for HHA comes from the cooperative behavior and hunting technique of Harris’ hawks in the wild, reg

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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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Mittal, Amit Kumar, and Kirti Mathur. "An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System." Indian Journal Of Science And Technology 17, no. 5 (2024): 397–408. http://dx.doi.org/10.17485/ijst/v17i5.2020.

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Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (W

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Al-Bazoon, Mustafa. "Harris Hawks Optimization for Optimum Design of Truss Structures with Discrete Variables." International Journal of Mathematical, Engineering and Management Sciences 6, no. 4 (2021): 1157–73. http://dx.doi.org/10.33889/ijmems.2021.6.4.069.

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This article investigates the use of Harris Hawks Optimization (HHO) to solve planar and spatial trusses with design variables that are discrete. The original HHO has been used to solve continuous design variables problems. However, HHO is formulated to solve optimization problems with discrete variables in this research. HHO is a population-based metaheuristic algorithm that simulates the chasing style and the collaborative behavior of predatory birds Harris hawks. The mathematical model of HHO uses a straightforward formulation and does not require tuning of algorithmic parameters and it is

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Song, Meijia, Heming Jia, Laith Abualigah, et al. "Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–23. http://dx.doi.org/10.1155/2022/4673665.

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One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving th

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Sun, Wei, Tian Peng, Yuanlin Luo, et al. "Hybrid short-term runoff prediction model based on optimal variational mode decomposition, improved Harris hawks algorithm and long short-term memory network." Environmental Research Communications 4, no. 4 (2022): 045001. http://dx.doi.org/10.1088/2515-7620/ac5feb.

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Abstract Runoff prediction is an important basis for rational allocation of basin water resources and plays a very important role in regional water resources management. In this study, a hybrid short-term runoff prediction model based on long short-term memory network (LSTM), improved Harris hawks optimization algorithm (IHHO) and optimal variational mode decomposition (OVMD) are proposed. Firstly, the original runoff data is decomposed into several sub-modes by OVMD, and then the sub-modes are reconstructed by phase space reconstruction (PSR). Secondly, the Harris hawks optimization algorithm

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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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Li, Sen, Ran Zhang, Yuanming Ding, Xutong Qin, Yajun Han, and Huiting Zhang. "Multi-UAV Path Planning Algorithm Based on BINN-HHO." Sensors 22, no. 24 (2022): 9786. http://dx.doi.org/10.3390/s22249786.

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Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HH

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Amit, Kumar Mittal, and Mathur Kirti. "An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System." Indian Journal of Science and Technology 17, no. 5 (2024): 397–408. https://doi.org/10.17485/IJST/v17i5.2020.

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Abstract <strong>Objectives:</strong> Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. <strong>Methods:</strong> Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection

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Husnain, Ghassan, Shahzad Anwar, Fahim Shahzad, et al. "An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs." Journal of Sensors 2022 (October 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/6790082.

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In recent years, intelligent vehicles with cutting-edge vehicular applications have grown in popularity, enabling the growth of Vehicular Ad hoc Networks (VANETs). Vehicular Ad hoc Networks (VANETs) are a network of vehicles that share and analyze real-time data and require a well-organized and efficient data delivery method. The stability of clusters and dynamic topology change in VANETs are the major issues in finding an optimal route amongst the vehicles. The cooperative approach and surprise pounce chasing technique of Harris Hawks in nature serve as the main sources of inspiration for Har

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Li, Tianlong, Zhijun Liu, Chen Zhang, et al. "Synthesis of Non-Uniform Spiral Antenna with Low Peak Sidelobe Level Using Enhanced Harris Hawks Optimization Algorithm." Electronics 13, no. 15 (2024): 2959. http://dx.doi.org/10.3390/electronics13152959.

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In this paper, to obtain antenna arrays with grating lobes suppression capability in wideband and achieve a low peak sidelobe level (PSLL), two non-uniform spiral antenna arrays and an enhanced Harris Hawks optimization (EHHO) algorithm are proposed. By controlling the parameters of the spiral line and sampling equidistant on the spiral line, the sampling points that make up the non-uniform array can be arranged in the plane uniformly and non-uniformly. The simulation results indicate that, because of this special arrangement, the non-uniform arrays obtain the capability of grating lobe suppre

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Et. al., Maddali M. V. M. Kumar,. "Energy Harvesting Wireless Sensor Network (Eh-Wsn) Based Modified Negatively Correlated Search Algorithm For Non-Convex Optimization Problems." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 6059–72. http://dx.doi.org/10.17762/turcomat.v12i10.5431.

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Network resource allotment is a significant concern for designing energy harvesting wireless sensor networks (EHWSNs). So, in this manuscript, Modified Negatively Correlated Search by Harris Hawks Optimization (MNCSHHO) algorithm is proposed for EH-WSNs with interference channel to solve the Non convex problems. It also used for optimizing data rates, energy transfers, and minimizing the total network delay. Initially, it deals with the complicated nonlinear constraints and also optimizes the data rates and energy transfer. By this total network delay can be minimized. The simulations are perf

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Cai, Cuicui, Chaochuan Jia, Yao Nie, Jinhong Zhang, and Ling Li. "A path planning method using modified harris hawks optimization algorithm for mobile robots." PeerJ Computer Science 9 (July 18, 2023): e1473. http://dx.doi.org/10.7717/peerj-cs.1473.

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Path planning is a critical technology that could help mobile robots accomplish their tasks quickly. However, some path planning algorithms tend to fall into local optimum in complex environments. A path planning method using a modified Harris hawks optimization (MHHO) algorithm is proposed to address the problem and improve the path quality. The proposed method improves the performance of the algorithm through multiple strategies. A linear path strategy is employed in path planning, which could straighten the corner segments of the path, making the obtained path smooth and the path distance s

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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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Tian, Fulin, Jiayang Wang, and Fei Chu. "Improved Multi-Strategy Harris Hawks Optimization and Its Application in Engineering Problems." Mathematics 11, no. 6 (2023): 1525. http://dx.doi.org/10.3390/math11061525.

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In order to compensate for the low convergence accuracy, slow rate of convergence, and easily falling into the trap of local optima for the original Harris hawks optimization (HHO) algorithm, an improved multi-strategy Harris hawks optimization (MSHHO) algorithm is proposed. First, the population is initialized by Sobol sequences to increase the diversity of the population. Second, the elite opposition-based learning strategy is incorporated to improve the versatility and quality of the solution sets. Furthermore, the energy updating strategy of the original algorithm is optimized to enhance t

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M. Abualhaj, Mosleh, Sumaya Nabil Alkhatib, Ahmad Adel Abu-Shareha, Adeeb M. Alsaaidah, and Mohammed Anbar. "Enhancing spam detection using Harris Hawks optimization algorithm." TELKOMNIKA (Telecommunication Computing Electronics and Control) 23, no. 2 (2025): 447. https://doi.org/10.12928/telkomnika.v23i2.26615.

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Elthokaby, Youssuf Ahmed, Ibrahim Abdelsalam, Naser Abdel-Rahim, and Islam Mohamed Abdealqawee. "Model-predictive control based on Harris Hawks optimization for split-source inverter." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 2348–58. http://dx.doi.org/10.11591/eei.v11i4.3823.

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This paper proposed a modified algorithm for controlling a single-phase split-source inverter. The proposed algorithm is a modified model predictive control based on Harris Hawks optimization, where the AC output voltage, the DC-link voltage, and the DC input current are controlled within one cost function. Hence, the discrete time models of both AC-side and DC-side are obtained. For proper operation of the modified MPC, each error term within the cost function has a weighting factor. Harris Hawks optimization technique is used to determine the weighting factors at each term of the cost functi

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Youssuf, Elthokaby, Abdelsalam Ibrahim, Abdel-Rahim Naser, and Mohamed Abdelqawee Islam. "Model-predictive control based on Harris Hawks optimization for split-source inverter." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 2348~2358. https://doi.org/10.11591/eei.v11i4.3823.

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This paper proposed a modified algorithm for controlling a single-phase split-source inverter. The proposed algorithm is a modified model predictive control based on Harris Hawks optimization, where the AC output voltage, the DC-link voltage, and the DC input current are controlled within one cost function. Hence, the discrete time models of both AC-side and DC-side are obtained. For proper operation of the modified MPC, each error term within the cost function has a weighting factor. Harris Hawks optimization technique is used to determine the weighting factors at each term of the cost functi

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Zhang, Ran, Sen Li, Yuanming Ding, Xutong Qin, and Qingyu Xia. "UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization." Sensors 22, no. 14 (2022): 5232. http://dx.doi.org/10.3390/s22145232.

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In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path pl

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Tang, Mingzhu, Zhonghui Peng, and Huawei Wu. "Fault Detection for Pitch System of Wind Turbine-Driven Doubly Fed Based on IHHO-LightGBM." Applied Sciences 11, no. 17 (2021): 8030. http://dx.doi.org/10.3390/app11178030.

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To address the issue of a large calculation and difficult optimization for the traditional fault detection of a wind turbine-based pitch control system, a fault detection model, based on LightGBM by the improved Harris Hawks optimization algorithm (light gradient boosting machine by the improved Harris Hawks optimization, IHHO-LightGBM) for the wind turbine-based pitch control system, is proposed in this article. Firstly, a trigonometric function model is introduced by IHHO to update the prey escape energy, to balance the global exploration ability and local development ability of the algorith

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Yang, Ting, Jie Fang, Chaochuan Jia, Zhengyu Liu, and Yu Liu. "An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism." PLOS ONE 18, no. 2 (2023): e0281636. http://dx.doi.org/10.1371/journal.pone.0281636.

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The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity o

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Yang, Junyi, Yutong Yao, and Donghe Yang. "Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking." Journal of Marine Science and Engineering 11, no. 7 (2023): 1456. http://dx.doi.org/10.3390/jmse11071456.

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Due to the complexity of the underwater environment, tracking underwater targets via traditional particle filters is a challenging task. To resolve the problem that the tracking accuracy of a traditional particle filter is low due to the sample impoverishment caused by resampling, in this paper, a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) is proposed. At the same time, the problem of particle filter underwater target feature construction and underwater target scale transformation is addressed, the corrected background-weighted histogram method is introduced i

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S, Mahima, Kiruthiga T, B. Rajesh Kumar, Kalaiselvan K, and Hakeem Ahmed Othman. "OPTIMIZING NETWORK LIFETIME IN WIRELESS SENSOR NETWORKS FOR EFFICIENT CLUSTER HEAD SECTION." ICTACT Journal on Communication Technology 15, no. 2 (2024): 3185–89. http://dx.doi.org/10.21917/ijct.2024.0474.

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Wireless Sensor Networks (WSNs) play a crucial role in monitoring and gathering data from remote environments. Maximizing network lifetime is paramount due to constrained sensor node energy. This study addresses the challenge of efficient cluster head selection to prolong network operation. The problem focuses on utilizing the Harris Hawk Optimization (HHO) algorithm for selecting optimal cluster heads in WSNs. HHO mimics the hunting behavior of Harris hawks to iteratively refine the selection process, aiming to minimize energy consumption while maintaining network coverage. The method involve

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Zhang, Yu, Yuhu Wu, Lianmin Li, and Zhongxiang Liu. "A Hybrid Energy Storage System Strategy for Smoothing Photovoltaic Power Fluctuation Based on Improved HHO-VMD." International Journal of Photoenergy 2023 (April 4, 2023): 1–13. http://dx.doi.org/10.1155/2023/9633843.

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To solve the problems of large fluctuation of photovoltaic output power affecting the safe operation of the power grid, a hybrid energy storage capacity configuration strategy based on the improved Harris hawks optimization algorithm optimizing variational mode decomposition (IHHO-VMD) is proposed. In this strategy, the improved Harris hawk optimization algorithm is used to adaptively select k and α in VMD parameters and decompose the photovoltaic output power and distinguish between correlated and uncorrelated modes. Similarly, the moving average method (MA) is used to extract the continuous

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Wang, Hongmin, Meng Wang, Dagang Li, Fuqin Deng, Zengxi Pan, and Yingying Song. "Gait Phase Recognition of Hip Exoskeleton System Based on CNN and HHO-SVM Model." Electronics 14, no. 1 (2024): 107. https://doi.org/10.3390/electronics14010107.

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Gait phase recognition is crucial for developing wearable lower-limb exoskeleton robots and is a prerequisite for the compliance control of lower-limb exoskeleton robots. Accurately estimating the gait phase is still a key challenge in exoskeleton control. To address these challenges, this study proposes a hybrid model that combines Convolutional Neural Networks (CNN) and Harris Hawks Optimization (HHO)—based Support Vector Machine (SVM). First, the collected sensor signals are preprocessed by normalization to reduce the differences in the data of the subjects. Then, a simplified CNN is used t

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