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Jiao, Shangbin, Chen Wang, Rui Gao, Yuxing Li, and Qing Zhang. "Harris Hawks Optimization with Multi-Strategy Search and Application." Symmetry 13, no. 12 (2021): 2364. http://dx.doi.org/10.3390/sym13122364.
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The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.
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XU, Xiaohan, Haima YANG, Heqing ZHENG, et al. "Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy." Wuhan University Journal of Natural Sciences 28, no. 6 (2023): 461–73. http://dx.doi.org/10.1051/wujns/2023286461.
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Because of the low convergence accuracy of the basic Harris Hawks algorithm, which quickly falls into the local optimal, a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy (TDHHO) is proposed. The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development. The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed. The difference variation strategy is used to mutate the individual position and calculate the fitness, and the fitness of the original individual position is compared. The greedy technique is used to select the one with better fitness of the objective function, which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value. The test function tests the TDHHO algorithm, and compared with other optimization algorithms, the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved. Finally, the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks (WSN) coverage optimization problems, and the feasibility of the TDHHO algorithm in practical application is further verified.
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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 is the most effective one. Moreover, the proposed chaotic HHO is compared to four metaheuristic algorithms in the literature using the CEC2019 set. Next, the proposed chaotic HHO is applied to three mechanical design problems, including pressure vessel, tension/compression spring, and three-bar truss system as benchmarks. The performances and results are compared with other popular algorithms in the literature. They show that the proposed chaotic HHO algorithm can compete with HHO and other algorithms on solving the given engineering problems very successfully.
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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 best point of the function. First, introduce the Harris Hawks optimization progressive rapid dives stage in the onlooker bee phase to speed up the algorithm convergence; Secondly, Cauchy reverse learning is added in the scout phase to make the algorithm development more promising areas in order to find a better solution; Finally, 13 standard test functions and CEC-C06 2019 benchmark test results are used to test the proposed HABC algorithm and compare with ABC, Markov Chain based artificial bee colony algorithm (MABC), dragonfly algorithm (DA), particle swarm optimization (PSO), learner performance based behavior algorithm (LPB), and fitness dependent optimizer (FDO). Compared with other algorithms, the convergence speed, optimization accuracy and algorithm success rate of the HABC algorithm are relatively excellent.</p> <p>&nbsp;</p>
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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 state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.
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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 interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.
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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 the transformation of the algorithm phase. Finally, a nonlinear control strategy is implemented to further improve the algorithm&rsquo;s efficiency. The experimental results on benchmark functions indicate that the performance of the MHHO algorithm outperforms other algorithms. In addition, to validate the performance of the MHHO algorithm in solving engineering problems, the proposed algorithm is applied to an indoor visible light positioning system, and the results show that the high precision positioning of the MHHO algorithm is obtained.</p> <p>&nbsp;</p>
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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 study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k -NN algorithm, support vector machine, and hybrid data mining methods in accuracy.
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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 cycle algorithm (WCA), butterfly optimization algorithm (BOA), bird swarm algorithm (BSA), crow search algorithm (CSA), grasshopper optimization algorithm (GOA), Harris Hawks Optimization (HHO), whale optimization algorithm (WOA), dandelion optimizer (DO) and fire hawks optimization (FHO) using IEEE CEC 2017, was performed. The experimental and analytical results show the adequate performance of the proposed algorithm.
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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 Hawks optimization algorithm is verified by testing it on the Reeves benchmark test set and comparing it with other algorithms.
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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 algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods.
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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: speed reducer optimization, pressure vessel optimization, cantilever beam optimization and tension/compression spring optimization. The statistical results and comparisons show that the HHO algorithm provides very promising and competitive results compared to others metaheuristic algorithms.
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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 ARHHO algorithm. Finally, the performance of our algorithm is evaluated by comparison with other well-known metaheuristic algorithms on 23 benchmark problems. Experimental results show that our algorithms performs better than the compared algorithms on most of the benchmark functions.
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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 on a recent real-world COVID-19 dataset from Kaggle. An augmented version of the COVID-19 dataset is also generated and experimentally shown to improve the quality of the solutions. Significant improvements are observed compared to existing state-of-the-art metaheuristic wrapper algorithms. We report better classification results with feature selection than when using the entire set of features. During experiments, a 98.15% prediction accuracy with a 45% reduction is achieved in the number of features. We successfully obtained new best solutions for this COVID-19 dataset.
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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 the Harris Hawk Optimization algorithm compared to other approaches, suggesting its suitability for real-world applications in smart city environments. Future directions for research in this area are also discussed.Keywords: Evolutionary algorithms, Vehicle routing, Smart cities, Optimization, Comparative analysis1.IntroductionSmart transportation has emerged as an indispensable necessity and solution in today's traffic-congested cities. Urbanization presents significant challenges, including traffic congestion, pollution, and inefficiencies in transportation systems. In response to these challenges, smart cities have been developed, leveraging Information and Communication Technology (ICT) to promote sustainable development approaches[1].Afundamental aspect of smart cities is the implementation of intelligent transportation systems, which optimize vehicle routing to minimize congestion, reduce travel time, and enhance overall efficiency. Evolutionary algorithms have gained prominence as effective tools for addressing complex optimization problems in smart transportation systems[2].This research focuses on improving vehicle routing in smart cities using evolutionary algorithms, with a particular emphasis on the Hybrid Harris's Hawks Optimization (HHO) algorithm[3]. The HHO algorithm is a gradient-independent optimization technique inspired by the cooperative behavior and agile pursuit of Harris's Hawks in nature, known as the "surprise pounce."
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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 mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.
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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 predict software faults, with some algorithms performing better than others depending on the dataset used. Our findings suggest that these approaches have potential to be used as a practical and efficient means for software fault prediction. Keywords— nature inspired algorithms; PSO; Ant Colony Optimization; Harris Hawks; Genetic Algorithm (GA); python programming; Jupyter Notebook; confusion matrix;
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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, a stochastic learning mechanism based on Gaussian mutation, and refracted opposition-based learning. At the same time, we conduct a comprehensive comparison between our proposed MSI-HHO algorithm with the standard HHO algorithm and five other well-known metaheuristic optimization algorithms. Extensive simulation experiments are conducted on both the 23 classical benchmark functions and the IEEE CEC 2020 benchmark functions. Then, the results of the non-parametric tests indicate that the MSI-HHO algorithm outperforms six other comparative algorithms at a significance level of 0.05 or greater. Additionally, the visualization analysis demonstrates the superior convergence speed and accuracy of the MSI-HHO algorithm, providing evidence of its robust performance.
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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 well-known multi-objective optimization problems has been used to evaluate the performance of the proposed NDSHHMO algorithm. The results of the NDSHHMO algorithm were verified against the results of an HHMO algorithm. Experimental results demonstrate the efficiency of the proposed NDSHHMO algorithm in terms of enhancing the ability of convergence toward the Pareto front and significantly improve the search ability of the HHMO.
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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 convergence rate, and appropriate exploitation-exploration balance. The RTH optimizer is compared with other algorithms to check and approve its performance. Using the proposed method, the root mean squared error (RMSE) between the model outputs and the measured voltage dataset was decreased to 8.12E-03, much better than all the other considered algorithms.
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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 position update was reduced. At the same time, the improved sine-trend search strategy was introduced to improve the search performance and reduce the out-of-bound rate. Then, a nonlinear jump strength combining escape energy and jump strength was proposed to improve the convergence accuracy of the algorithm. Finally, the simulation experiment was carried out on the test function and the path planning application of a 2D grid map. The results show that the Improved Harris Hawks Optimization algorithm is more competitive in solving accuracy, convergence speed, and non-origin symmetric interval search efficiency, and verifies the feasibility and effectiveness of the Improved Harris Hawks Optimization in the path planning of a grid map.
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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 exploration and exploitation of the algorithm. Third, Cauchy mutation on the optimal individual in the exploration phase is performed, and the characteristics of the Cauchy distribution to enhance the diversity of the population are used, which can effectively prevent the algorithm from falling into the local optimum. Fourth, the local optimization ability of the algorithm by using the ergodicity of the chaotic system in the exploitation phase to perform a chaotic local search for the optimal individual is enhanced, which can effectively jump out after the algorithm falls into the local optimum. Finally, we use the elite individuals of the population to guide the position update of the population’s individuals, fully communicate with the dominant individuals, and speed up the convergence speed of the algorithm. Through the simulation experiments on CCCHHO with 11 different benchmark functions, CCCHHO is better than the gray wolf optimization algorithm (GWO), the Salp swarm algorithm (SSA), the ant lion optimization algorithm (ALO), and three improved HHO algorithms in terms of convergence speed and optimization accuracy, whether it is a unimodal benchmark function or a multimodal benchmark function. The experimental results show that CCCHHO has excellent algorithm efficiency and robustness.
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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 of its high diversity, fast convergence rate, and appropriate exploitation-exploration balance. The RTH optimizer is compared with other algorithms to check and approve its performance. Using the proposed method, the root mean squared error (RMSE) between the model outputs and the measured voltage dataset was decreased to 8.12E-03, much better than all the other considered algorithms.
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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.
Full textAbstract:
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, regarded as the “surprise pounce”. In this clever strategy, distinct hawks work together in order to pounce on their prey from multiple pathways, aiming to catch them by surprise. Harris' hawks are capable of identifying various chasing patterns, influenced by the dynamic nature of the situation and the escape tactics of the prey. The mentioned approach is examined on IEEE 33 node RDS. The effectiveness of this approach, in comparison to other established methods, is demonstrated via simulation results that assess total losses, costs, and savings. Additionally, the statistical analysis is conducted to validate the potency of the advised HHA.
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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.
Full textAbstract:
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 Search, Multi-Verse Optimizer, and JAYA algorithms. Results showed that this novel cholesterol algorithm implementation can compete effectively with the best-known solution to test functions.
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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.
Full textAbstract:
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 (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. Findings: The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. Novelty: The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). Keywords: Power generation, Solar power forecasting, Whale optimization algorithm, Long ShortTerm Memory, Harris hawk's optimization
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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.
Full textAbstract:
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 a robust algorithm in exploitation. The performance of HHO is evaluated using five benchmark structural problems and the final designs are compared with ten state-of-the-art algorithms. The statistical outcomes (average and standard deviation of final designs) show that HHO is quite consistent and robust in solving truss structure optimization problems. This is an important characteristic that leads to better confidence in the final solution from a single run of the algorithm for an optimization problem.
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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.
Full textAbstract:
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 the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.
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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.
Full textAbstract:
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 is improved by the chaos map and the hill climbing algorithm. Then, the LSTM model is established for each sub-mode, and the improved Harris hawks optimization algorithm (IHHO) is used to optimize the number of hidden layer neurons and learning rate of the LSTM network. Finally, the results of all sub-modes are combined to obtain the finally runoff prediction result. In this study, seven control models are constructed and compared with the proposed model to verify the effectiveness of the proposed model in runoff prediction.
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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.
Full textAbstract:
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 algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. The suggested method optimizes the SVM model parameters while also locating the optimal features subset. We ran the proposed approach HHO-SVM on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. The experimental results demonstrate the supremacy of the proposed HHO-SVM in terms of three performance metrics: feature selection accuracy, runtime, and number of selected features. The suggested method is contrasted with four well-known algorithms for verification: firefly (FF) algorithm, genetic algorithm (GA), grasshopper optimization algorithm (GOA), and particle swarm algorithm (PSO). The implementation of the proposed HHO-SVM approach reveals 99.967% average accuracy.
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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.
Full textAbstract:
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 HHO and embed it into the energy function, which balances the algorithm’s multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA).
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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.
Full textAbstract:
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 in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. <strong>Findings:</strong> The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. <strong>Novelty:</strong> The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). <strong>Keywords:</strong> Power generation, Solar power forecasting, Whale optimization algorithm, Long Short­Term Memory, Harris hawk's optimization
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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.
Full textAbstract:
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 Harris Hawks Optimization. In this technique, several hawks work together to attack a victim from various angles to surprise it. Due to the unpredictable nature of situations and the prey’s fleeing movements, Harris Hawks can exhibit a variety of intelligent strategies. This study proposes a novel route clustering optimization technique that takes into account communication range, the number of nodes, velocity, orientations, and grid size. To create and evaluate ideal cluster head (CH), the proposed method is based on Harris Hawks Intelligent Optimization Algorithm for route Clustering (iCHHO) which finds optimal and reliable routes amongst the vehicles. Other state-of-the-art methods, such as the Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimization (GWO), and Whale Optimization Algorithm (WOACNET), are utilized to evaluate and validate the proposed method. Our findings show that the developed method outperforms other current methods in terms of number of clusters, variable communication ranges, network size, and the number of vehicles. Furthermore, the statistical analysis concludes that the proposed method improves cluster optimization by 79% and increases cluster stability by an adjusted R -squared of 91.22.
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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.
Full textAbstract:
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 suppression in wideband or wide spacing arrangement when compared to the classic uniform array. In addition, to obtain lower PSLL, the Harris Hawks optimization (HHO) algorithm is used for array synthesis because of its diversity of search methods. By employing the step-type taper distribution strategy and the migration strategy, the algorithm’s search ability is enhanced, and the simulation results indicate the EHHO algorithm obtains a better solution in terms of the PSLL than other algorithms. A simple patch antenna is designed to build the non-uniform spiral arrays synthesized by the EHHO algorithm. The calculation and simulation results validate the superior performance of the proposed algorithm.
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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.
Full textAbstract:
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 performed using Network Simulator (NS2) to validate the performance of the proposed Modified Negatively Correlated Search by Harris Hawks Optimization (MNCSHHO) algorithm and it provide better results such as high network life time as 1.09, 2.34, high throughput as 0.65, 1.024, energy transfer as 24%, 51%, low delay as 20.29%, 42.416%, low drop as 2.34%, 3.3455% and low overhead as 40.52%, 23.4% are compared with the existing algorithms like EDS-NCS, convex approximation respectively.
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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.
Full textAbstract:
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 short. Then, to avoid getting into the local optimum, a local search update strategy is applied to the HHO algorithm. In addition, a nonlinear control strategy is also used to improve the convergence accuracy and convergence speed. The performance of the MHHO method was evaluated through multiple experiments in different environments. Experimental results show that the proposed algorithm is more efficient in path length and speed of convergence than the ant colony optimization (ACO) algorithm, improved sparrow search algorithm (ISSA), and HHO algorithms.
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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.
Full textAbstract:
<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 suggested metaheuristic algorithms that has been demonstrated to be used more efficiently in facing some optimization problems. The suggested method optimizes the SVM model parameters while also locating the optimal features subset. </span><span lang="EN-AU">We ran the proposed approach </span><span lang="EN-US">HHO-SVM </span><span lang="EN-AU">on real biomedical datasets with 17 types of cancer for Iraqi patients in 2010-2012. </span><span lang="EN-US">The experimental results demonstrate the supremacy of the proposed HHO-SVM in terms of three performance metrics: feature selection accuracy, runtime, and number of selected features. The suggested method is contrasted with four well-known algorithms for verification: firefly (FF) algorithm, genetic algorithm (GA), grasshopper optimization algorithm (GOA), and particle swarm algorithm (PSO). The implementation of the proposed HHO-SVM approach reveals 99.967% average accuracy.</span>
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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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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.
Full textAbstract:
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 the exploration and exploitation capability of the algorithm in a nonlinear update manner. Finally, the Gaussian walk learning strategy is introduced to avoid the algorithm being trapped in a stagnant state and slipping into a local optimum. We perform experiments on 33 benchmark functions and 2 engineering application problems to verify the performance of the proposed algorithm. The experimental results show that the improved algorithm has good performance in terms of optimization seeking accuracy, the speed of convergence, and stability, which effectively remedies the defects of the original algorithm.
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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.
Full textAbstract:
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 planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.
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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.
Full textAbstract:
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 function. The proposed algorithm is validated using MATLAB/Simulink. Simulation results show that the system has succeeded in controlling AC load voltage, input current, and achieving constant DC-link voltage over a wide operating range.
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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.
Full textAbstract:
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 function. The proposed algorithm is validated using MATLAB/Simulink. Simulation results show that the system has succeeded in controlling AC load voltage, input current, and achieving constant DC-link voltage over a wide operating range.
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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.
Full textAbstract:
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 algorithm. In this model, the fault detection false alarm rate is used as the fitness function, and the two parameters are used as the optimization objects of the improved Harris Hawks optimization algorithm, to optimize the parameters, so as to achieve the global optimal parameters to improve the performance of the fault detection model. Three different fault data of the pitch control system in actual operations of domestic wind farms are used as the experimental data, the Pearson correlation analysis method is introduced, and the wind turbine power output is taken as the main state parameter, to analyze the correlation degree of all the characteristic variables of the data and screen the important characteristic variables out, so as to achieve the effective dimensionality reduction process of the data, by using the feature selection method. Three established fault detection models are selected and compared with the proposed method, to verify its feasibility. The experimental data indicate that compared with other algorithms, the fault detecting ability of the proposed model is improved in all aspects, and the false alarm rate and false negative rate are lower.
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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.
Full textAbstract:
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 of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms.
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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.
Full textAbstract:
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 into underwater target feature recognition, and the scale filter is combined to realize target scaling transformation during tracking. In addition, to enhance the computational speed of underwater target tracking, this paper constructs a nonlinear escape energy using the Harris hawks algorithm in order to balance the exploration and exploitation processes. Based on the proposed HHOPF tracker, we performed detection and evaluation using the Underwater Object Tracking (UOT100) vision database. The proposed method is compared with evolution-based tracking algorithms and particle filters, as well as with recent tracker-based correlation filters and some other state-of-the-art tracking methods. By comparing the results of tracking using the test data sets, it is determined that the presented algorithm improves the overlap accuracy and tracking accuracy by 11% compared with other algorithms. The experiments demonstrate that the presented HHOPF visual tracking provides better tracking results.
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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.
Full textAbstract:
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 involves initializing Harris hawks (representing potential cluster heads) within the sensor field, where their movements simulate search and convergence towards optimal locations. Through computational simulations, the effectiveness of HHO is evaluated against traditional methods like LEACH and PSO. Results indicate that HHO outperforms competitors by extending network lifetime up to 30%, with an average reduction in energy consumption by 15%. Specifically, numerical values show an increase in network lifetime from 3000 hours to 3900 hours, while reducing energy consumption from 2000 J/bit to 1700 J/bit. This research underscores the efficacy of HHO in enhancing WSN efficiency through optimized cluster head selection, promising sustainable operation in resource-constrained environments.
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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.
Full textAbstract:
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 component signal in the uncorrelated mode, and it is reconstructed with the related mode as the grid-connected power that meets the national standard. The hybrid energy storage system (HESS) is used to stabilize the fluctuation component signal. The minimum annual configuration cost of the energy storage system is established as the objective function. The simulation results show that the improved algorithm reduces the cost of the hybrid energy storage system by 6.15% compared with the original algorithm, suppresses the power fluctuation, and improves the economy and stability of the system.
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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.
Full textAbstract:
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 to automatically extract more discriminative features from the dataset. These features are classified using SVM instead of the softmax layer in CNN. In addition, an improved Harris hawk optimization (HHO) algorithm is used to optimize the SVM classification process. This model can accurately identify the heel strike (HS), flat foot (FF), heel off (HO), and swing (SW) phases of the gait cycle. The experimental results show that the CNN-HHO-SVM algorithm can achieve an average phase recognition accuracy of 96.03% for seven subjects in the self-built dataset, which is superior to the traditional method that relies on manually extracting time-frequency features. In addition, the F1-score and macro-recall of the CNN-HHO-SVM algorithm are better than those of other algorithms, which verifies the superiority of the algorithm.