Relevant bibliographies by topics / Hawks algorithm

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  2. Dissertations / Theses
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  5. Conference papers

Academic literature on the topic 'Hawks algorithm'

Author: Grafiati
Published: 2 June 2025
Last updated: 7 July 2025

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Journal articles on the topic "Hawks algorithm"

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 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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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 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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3

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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4

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> </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’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> </p>
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8

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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Dissertations / Theses on the topic "Hawks algorithm"

1

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

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The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals' activities in the social network. The inference of relationship between users/nodes or groups of users/nodes could be further complicated when activities are interval-censored, that is, when one only observed the number of activities that occurred in certain time windows. The same phenomenon happens in the online advertisement world where the advertisers often offer a set of advertisement impressions and observe a set of conversions (i.e. product/service adoption). In this case, the advertisers desire to know which advertisements best appeal to the customers and most importantly, their rate of conversions. Inspired by these challenges, we investigated inference algorithms that efficiently recover user relationships in both cases: time-stamped data and interval-censored data. In case of time-stamped data, we proposed a novel algorithm called NetCodec, which relies on a Hawkes process that models the intertwine relationship between group participation and between-user influence. Using Bayesian variational principle and optimization techniques, NetCodec could infer both group participation and user influence simultaneously with iteration complexity being O((N+I)G), where N is the number of events, I is the number of users, and G is the number of groups. In case of interval-censored data, we proposed a Monte-Carlo EM inference algorithm where we iteratively impute the time-stamped events using a Poisson process that has intensity function approximates the underlying intensity function. We show that that proposed simulated approach delivers better inference performance than baseline methods. In the advertisement problem, we propose a Click-to-Conversion delay model that uses Hawkes processes to model the advertisement impressions and thinned Poisson processes to model the Click-to-Conversion mechanism. We then derive an efficient Maximum Likelihood Estimator which utilizes the Minorization-Maximization framework. We verify the model against real life online advertisement logs in comparison with recent conversion rate estimation methods. To facilitate reproducible research, we also developed an open-source software package that focuses on various Hawkes processes proposed in the above mentioned works and prior works. We provided efficient parallel (multi-core) implementations of the inference algorithms using the Bayesian variational inference framework. To further speed up these inference algorithms, we also explored distributed optimization techniques for convex optimization under the distributed data situation. We formulate this problem as a consensus-constrained optimization problem and solve it with the alternating direction method for multipliers (ADMM). It turns out that using bipartite graph as communication topology exhibits the fastest convergence.
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Haghdan, Maysam. "Hawkes Process Models for Unsupervised Learning on Uncertain Event Data." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1503679661498849.

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3

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

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La dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds influents, la détection des communautés, la détection des tendances, etc. Cette thèse est donc basée sur ces deux facettes de la dissémination d'information: nous développons d'abord des cadres théoriques mathématiquement solides pour étudier les relations entre les personnes et l'information, et dans un deuxième moment nous créons des outils responsables pour une exploration plus cohérente des liens cachés dans ces relations. Les outils théoriques développés ici sont les modèles de dynamique d'opinions et de dissémination d'information, où nous étudions le flot d'informations des utilisateurs dans les réseaux sociaux, et les outils pratiques développés ici sont un nouveau algorithme de détection de communautés et un nouveau algorithme de détection de tendances dans les réseaux sociaux<br>Our aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topics
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4

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

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La dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds influents, la détection des communautés, la détection des tendances, etc. Cette thèse est donc basée sur ces deux facettes de la dissémination d'information: nous développons d'abord des cadres théoriques mathématiquement solides pour étudier les relations entre les personnes et l'information, et dans un deuxième moment nous créons des outils responsables pour une exploration plus cohérente des liens cachés dans ces relations. Les outils théoriques développés ici sont les modèles de dynamique d'opinions et de dissémination d'information, où nous étudions le flot d'informations des utilisateurs dans les réseaux sociaux, et les outils pratiques développés ici sont un nouveau algorithme de détection de communautés et un nouveau algorithme de détection de tendances dans les réseaux sociaux<br>Our aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topics
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5

Rambaldi, Marcello. "Some applications of Hawkes point processes to high frequency finance." Doctoral thesis, Scuola Normale Superiore, 2017. http://hdl.handle.net/11384/85718.

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

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L'objectif de cette thèse est de construire des algorithmes capables de simuler l'activité d'un réseau de neurones. L'activité du réseau de neurones peut être modélisée par le train de spikes de chaque neurone, qui sont représentés par un processus ponctuel multivarié. La plupart des approches connues pour simuler des processus ponctuels rencontrent des difficultés lorsque le réseau sous-jacent est de grande taille.Dans cette thèse, nous proposons de nouveaux algorithmes utilisant un nouveau type de décomposition de Kalikow. En particulier, nous présentons un algorithme permettant de simuler le comportement d'un neurone intégré dans un réseau neuronal infini sans simuler l'ensemble du réseau. Nous nous concentrons sur la preuve mathématique que notre algorithme renvoie les bons processus ponctuels et sur l'étude de sa condition d'arrêt. Ensuite, une preuve constructive montre que cette nouvelle décomposition est valable pour divers processus ponctuels.Enfin, nous proposons des algorithmes, qui peuvent être parallélisés et qui permettent de simuler une centaine de milliers de neurones dans un graphe d'interaction complet, sur un ordinateur portable. Plus particulièrement, la complexité de cet algorithme semble linéaire par rapport au nombre de neurones à simuler<br>The goal of this thesis is to construct algorithms which are able to simulate the activity of a neural network. The activity of the neural network can be modeled by the spike train of each neuron, which are represented by a multivariate point processes. Most of the known approaches to simulate point processes encounter difficulties when the underlying network is large.In this thesis, we propose new algorithms using a new type of Kalikow decomposition. In particular, we present an algorithm to simulate the behavior of one neuron embedded in an infinite neural network without simulating the whole network. We focus on mathematically proving that our algorithm returns the right point processes and on studying its stopping condition. Then, a constructive proof shows that this new decomposition holds for on various point processes.Finally, we propose algorithms, that can be parallelized and that enables us to simulate a hundred of thousand neurons in a complete interaction graph, on a laptop computer. Most notably, the complexity of this algorithm seems linear with respect to the number of neurons on simulation
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Books on the topic "Hawks algorithm"

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

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The Handbook of Privacy Studies is the first book in the world that brings together several disciplinary perspectives on privacy, such as the legal, ethical, medical, informatics and anthropological perspective. Privacy is in the news almost every day: mass surveillance by intelligence agencies, the use of social media data for commercial profit and political microtargeting, password hacks and identity theft, new data protection regimes, questionable reuse of medical data, and concerns about how algorithms shape the way we think and decide. This book offers interdisciplinary background information about these developments and explains how to understand and properly evaluate them. The book is set up for use in interdisciplinary educational programmes. Each chapter provides a structured analysis of the role of privacy within that discipline, its characteristics, themes and debates, as well as current challenges. Disciplinary approaches are presented in such a way that students and researchers from every scientific background can follow the argumentation and enrich their own understanding of privacy issues.
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McCandless, Luke. Tinder Code: Secret Hacks to Crack the Algorithm and Increase Your Matches 10x. Independently Published, 2019.

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

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

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Book chapters on the topic "Hawks algorithm"

1

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

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

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

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

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

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

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

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

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

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Zhang, Yunyi, and Shuyuan Jin. "Hacks Hit the Phish: Phish Attack Detection Based on Hacks Search." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86137-7_33.

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Conference papers on the topic "Hawks algorithm"

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

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

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

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

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

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

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

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

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

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Astaomar, Seraj, and Bilgehan Erkal. "Improving the Performance of an Incremental Conductance MPPT Algorithm Using Harris-Hawks Optimization in Photovoltaic Systems." In 2024 Global Energy Conference (GEC). IEEE, 2024. https://doi.org/10.1109/gec61857.2024.10881949.

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You might also be interested in the extended bibliographies on the topic 'Hawks algorithm' for particular source types:
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