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Journal articles on the topic 'Chaotic grey wolf optimization'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kohli, Mehak, and Sankalap Arora. "Chaotic grey wolf optimization algorithm for constrained optimization problems." Journal of Computational Design and Engineering 5, no. 4 (2017): 458–72. http://dx.doi.org/10.1016/j.jcde.2017.02.005.

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Abstract The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization

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Ramana, Ramana, K. Kavitha, Smita Rani Sahu, B. Manideep, T. Ravi Kumar, and Nibedan Panda. "An Improved Chaotic Grey Wolf Optimization Algorithm (CGWO)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 341–48. http://dx.doi.org/10.17762/ijritcc.v11i11s.8161.

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Grey Wolf Optimization (GWO) is a new type of swarm-based technique for dealing with realistic engineering design constraints and unconstrained problems in the field of metaheuristic research. Swarm-based techniques are a type of population-based algorithm inspired by nature that can produce low-cost, quick, and dependable solutions to a wider variety of complications. It is the best choice when it can achieve faster convergence by avoiding local optima trapping. This work incorporates chaos theory with the standard GWO to improve the algorithm's performance due to the ergodicity of chaos. The

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Qin, Hongwu, Lizheng Wang, Muxuan Sui, and Chunyou Si. "Research on Grey Wolf Optimization Algorithm Based on Adaptive Adjustment Strategy." Journal of Physics: Conference Series 2395, no. 1 (2022): 012075. http://dx.doi.org/10.1088/1742-6596/2395/1/012075.

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Abstract A grey wolf optimization algorithm based on an adaptive adjustment strategy (Improvements-Grey Wolf Optimization, IGWO) is proposed in this paper to address the issues with the Grey Wolf Optimization (GWO) algorithm, which has a slow convergence speed in the later stages and where the local search and global search cannot be taken into effective balance. First, a chaotic logistic map is used to initialize the population. Next, an inverse trigonometric function-based mathematical model is developed to achieve convergence purposes. Finally, a new location update method is used to update

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Cao, Yalan, Xiaohong Zhang, and Zuyang Shen. "Capacity optimization configuration of multi-source independent microgrids based on TLC-GOLD-GWO algorithm." Journal of Physics: Conference Series 2831, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1742-6596/2831/1/012004.

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Abstract To guarantee the economic efficiency and reliability of standalone microgrids, this paper introduces an enhanced Grey Wolf optimization algorithm (GWO) [1] that leverages chaos mapping techniques, nonlinear convergence factor, and golden operator for solving multi-source capacity allocation problems. Taking the reliability of the power supply as a significant constraint, an optimization model is constructed with the minimization of the annual total economic cost as the objective function. Given the issues of local convergence and premature convergence encountered during the solution p

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Xu, Xiaoguang, Miao Wang, Ping Xiao, Jiale Ding, and Xiaoyu Zhang. "In-Wheel Motor Control System for Four-Wheel Drive Electric Vehicle Based on CR-GWO-PID Control." Sensors 23, no. 19 (2023): 8311. http://dx.doi.org/10.3390/s23198311.

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In order to improve the driving performance of four-wheel drive electric vehicles and realize precise control of their speed, a Chaotic Random Grey Wolf Optimization-based PID in-wheel motor control algorithm is proposed in this paper. Based on an analysis of the structural principles of electric vehicles, mathematical and simulation models for the whole vehicle are established. In order to improve the control performance of the hub motor, the traditional Grey Wolf Optimization algorithm is improved. In particular, an enhanced population initialization strategy integrating sine and cosine rand

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Liu, Qunjie, and Hongxing Wang. "UAV 3D path planning based on improved grey wolf optimization algorithm." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 113–16. http://dx.doi.org/10.54097/fcis.v3i1.6344.

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In this paper, an improved grey wolf optimization algorithm is proposed for the research of UAV path planning in a complex 3D environment. Firstly, a new nonlinear convergence factor is proposed to balance the performance of global search and local development. Secondly, a cubic chaotic mapping is adopted to initialize the wolf population, diversifying the population while improving the uniformity of the population distribution. Finally, a mutation operation is introduced to mutate the individual gray wolf, which enhances the ability of the algorithm to jump out of the local optimum. Three-dim

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Hou, Yuxiang, Huanbing Gao, Zijian Wang, and Chuansheng Du. "Improved Grey Wolf Optimization Algorithm and Application." Sensors 22, no. 10 (2022): 3810. http://dx.doi.org/10.3390/s22103810.

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This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the Gaussian distribution change curve to balance the global and local searchability. In addition, an improved dynamic proportional weighting strategy is proposed that can update the positions of grey wolves so

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Xiao, Lingfei, Min Xu, Yuhan Chen, and Yusheng Chen. "Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network." Applied Sciences 9, no. 6 (2019): 1254. http://dx.doi.org/10.3390/app9061254.

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In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic s

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Aznavourian, Ronald, Guillaume Demesy, Sébastien Guenneau, and Julien Marot. "Electromagnetic cloak design with mono-objective and bi-objective optimizers: seeking the best tradeoff between protection and invisibility." EPJ Applied Metamaterials 11 (2024): 11. http://dx.doi.org/10.1051/epjam/2023003.

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We revisit the design of cloaks, without resorting to any geometric transform. Cancellation techniques and anomalous resonances have been applied for this purpose. Instead of a deductive reasoning, we propose a novel mono-objective optimization algorithm, namely a ternary grey wolf algorithm, and we adapt a bi-objective optimization algorithm. Firstly, the proposed chaotic ternary grey wolf algorithm searches three-valued spaces for all permittivity values in the cloak while minimizing the summation of a protection criterion and an invisibility criterion. Secondly, a bi-objective genetic algor

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Wang, Shipeng, Xiaoping Yang, Xingqiao Wang, and Zhihong Qian. "A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization." Sensors 19, no. 12 (2019): 2735. http://dx.doi.org/10.3390/s19122735.

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The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embe

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Kong, Xiaohong, Yunhang Yao, Wenqiang Yang, Zhile Yang, and Jinzhe Su. "Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm." Machines 10, no. 11 (2022): 1100. http://dx.doi.org/10.3390/machines10111100.

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The flexible job shop scheduling problem (FJSP) is of great importance for realistic manufacturing, and the problem has been proven to be NP-hard (non-deterministic polynomial time) because of its high computational complexity. To optimize makespan and critical machine load of FJSP, a discrete improved grey wolf optimization (DIGWO) algorithm is proposed. Firstly, combined with the random Tent chaotic mapping strategy and heuristic rules, a hybrid initialization strategy is presented to improve the quality of the original population. Secondly, a discrete grey wolf update operator (DGUO) is des

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Zhang, Sen, Qifang Luo, and Yongquan Zhou. "Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method." International Journal of Computational Intelligence and Applications 16, no. 02 (2017): 1750012. http://dx.doi.org/10.1142/s1469026817500122.

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To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and hig

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Xu, Jianzhong, Fu Yan, Oluwafolakemi Grace Ala, Lifei Su, and Fengshu Li. "Chaotic dynamic weight grey wolf optimizer for numerical function optimization." Journal of Intelligent & Fuzzy Systems 37, no. 2 (2019): 2367–84. http://dx.doi.org/10.3233/jifs-182706.

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Wei, Sun. "Transformer Fault Diagnosis Based on DLH-GWO-SVM." Journal of Physics: Conference Series 2527, no. 1 (2023): 012050. http://dx.doi.org/10.1088/1742-6596/2527/1/012050.

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Abstract In the field of transformer fault diagnosis, there are many diagnostic methods. These diagnostic methods are either not high accuracy or are used too long. In this paper, a dimension learning-based hunting (DLH) search strategy optimization grey wolf algorithm support vector machine (DLH-GWO-SVM) model was proposed. In the process of initializing the grey wolf population, logistic chaotic mapping was used to improve the quality of the initial wolf position. In the Wolf screening process, the DLH search strategy was adopted to solve the problem that GWO was prone to fall into the local

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15

Chen, Long, Diju Gao, and Qimeng Xue. "Energy Management Strategy of Hybrid Ships Using Nonlinear Model Predictive Control via a Chaotic Grey Wolf Optimization Algorithm." Journal of Marine Science and Engineering 11, no. 9 (2023): 1834. http://dx.doi.org/10.3390/jmse11091834.

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Reducing energy consumption and carbon emissions from ships is a major concern. The development of hybrid technologies offers a new direction for the rational distribution of energy. Therefore, this paper establishes a torque model for internal combustion engines and motors based on first principles and fitting the data collected from the test platform; in turn, it develops a model for fuel consumption and carbon emissions. Furthermore, the effect of irregular waves using an extended Kalman filter is estimated as well as feedback to the controller as a disturbance variable. Then, a parallel hy

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Lu, Chao, Liang Gao, Xinyu Li, Chengyu Hu, Xuesong Yan, and Wenyin Gong. "Chaotic-based grey wolf optimizer for numerical and engineering optimization problems." Memetic Computing 12, no. 4 (2020): 371–98. http://dx.doi.org/10.1007/s12293-020-00313-6.

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Mehmood, Khizer, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, Khalid Mehmood Cheema, and Muhammad Asif Zahoor Raja. "Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model." Biomimetics 8, no. 2 (2023): 141. http://dx.doi.org/10.3390/biomimetics8020141.

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In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for AR

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Zhao, Kunxia, Yan Liu, and Kui Hu. "Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm." Electronics 12, no. 19 (2023): 4087. http://dx.doi.org/10.3390/electronics12194087.

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The grey wolf optimization (GWO) algorithm is a new nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. In this paper, the GWO algorithm is improved to overcome previous shortcomings of being easily trapped in local optima and having a low convergence rate. The proposed enhancement of the GWO algorithm utilizes logistic-tent double mapping to generate initialized populations, which enhances its global search capability and convergence rate. This improvement is called the nonlinear chaotic grey wolf optimization (NCGWO) algorithm. The p

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Abed, Qutaiba K., and Waleed A. Mahmoud Al-Jawher. "New Colorful Image Encryption Method Using Triple Chaotic Maps and Grey Wolf Optimization (GWO)." Journal Port Science Research 7, no. 3 (2024): 228–45. http://dx.doi.org/10.36371/port.2024.3.11.

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A novel image encryption algorithm employing triple chaotic maps has been developed to address the shortcomings of existing methods in terms of security and efficiency. The algorithm leverages the interconnectivity of color channels in images, using distinct keys to disrupt pixel correlations within each channel. The three chaotic maps utilized URUK, WAM, and Nahrain to generate two sets of keys. The first set is used to shuffle pixel positions, creating scrambled channels. Subsequently, the second set is applied to diffuse these scrambled channels independently. A gray wolf optimization (GWO)

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Cisternas-Caneo, Felipe, Broderick Crawford, Ricardo Soto, Giovanni Giachetti, Álex Paz, and Alvaro Peña Fritz. "Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics." Mathematics 12, no. 2 (2024): 262. http://dx.doi.org/10.3390/math12020262.

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Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtain better results. In the present work, chaotic maps are used to modify the behavior of the binarization rules that allow continuous metaheuristics to solve binary combinatorial optimization problems. In particular, seven different chaotic maps, three different binarization rules, and three continuous metaheuristics are used, which are the Sine Cosine Algorith

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Zhang, Yang, Ziying Liu, Mingfeng Zhou, Sicheng Li, Jiaxuan Li, and Zhun Cheng. "PMSM Parameter Identification Based on Chaotic Adaptive Search Grey Wolf Optimization Algorithm." Progress In Electromagnetics Research C 140 (2024): 117–26. http://dx.doi.org/10.2528/pierc23110703.

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Khujamatov, Halimjon, Mohaideen Pitchai, Alibek Shamsiev, Abdinabi Mukhamadiyev, and Jinsoo Cho. "Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks." Sensors 24, no. 13 (2024): 4406. http://dx.doi.org/10.3390/s24134406.

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As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale

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Wang, Xinchen, Shaorong Wang, Jiaxuan Ren, Zhaoxia Song, Shun Zhang, and Hupeng Feng. "Optimizing Economic Dispatch for Microgrid Clusters Using Improved Grey Wolf Optimization." Electronics 13, no. 16 (2024): 3139. http://dx.doi.org/10.3390/electronics13163139.

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With the rapid development of renewable energy generation in recent years, microgrid technology has increasingly emerged as an effective means to facilitate the integration of renewable energy. To efficiently achieve optimal scheduling for microgrid cluster (MGC) systems while guaranteeing the safe and stable operation of a power grid, this study, drawing on actual electricity-consumption patterns and renewable energy generation in low-latitude coastal areas, proposes an integrated multi-objective coordinated optimization strategy. The objective function includes not only operational costs, en

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Wang, Yingxun, Yan Ma, Zhihao Cai, and Jiang Zhao. "Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer." Transactions of the Institute of Measurement and Control 42, no. 9 (2020): 1675–89. http://dx.doi.org/10.1177/0142331219894401.

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In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor tr

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Xu, Zhe, Haichuan Yang, Jiayi Li, Xingyi Zhang, Bo Lu, and Shangce Gao. "Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms." IEEE Access 9 (2021): 77416–37. http://dx.doi.org/10.1109/access.2021.3083220.

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Xu, Wan, Dongting Liu, Ao Nie, Junqi Wang, and Shijie Liu. "Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm." Applied Sciences 15, no. 10 (2025): 5232. https://doi.org/10.3390/app15105232.

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Given that the traditional optimization algorithm (GWO) often encounters problems like local optimum, and the convergence efficiency is not satisfactory in the path planning task of multiple mobile robots, an improved grey wolf optimization algorithm (LAPGWO) based on the combination of the logistic chaotic mapping and the artificial potential field method (APF) is proposed. Firstly, the LAPGWO algorithm uses logistic chaotic mapping to initialize the scale of grey wolves, improving the diversity of the population distribution. Secondly, the potential field function of APF is introduced to gui

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Yang, Hong, Lipeng Gao, and Guohui Li. "Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine." Complexity 2020 (April 24, 2020): 1–17. http://dx.doi.org/10.1155/2020/6947059.

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Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper use

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Liu, Lili, Longhai Li, Heng Nian, Yixin Lu, Hao Zhao, and Yue Chen. "Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning." Electronics 12, no. 19 (2023): 4026. http://dx.doi.org/10.3390/electronics12194026.

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In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence fa

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Yang, Wenqiang, Yihang Zhang, Xinxin Zhu, Kunyan Li, and Zhile Yang. "Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm." Energies 17, no. 6 (2024): 1491. http://dx.doi.org/10.3390/en17061491.

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The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wo

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J., Shreyas, Reena S. Kharat, Rajesh N. Phursule, et al. "Chaotic grey wolf optimization based framework for efficient task scheduling in cloud fog computing." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2066–76. https://doi.org/10.11591/eei.v14i3.8098.

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Task scheduling is an essential component of any cloud computing architecture that seeks to cater to the requirements of its users in the most effective manner possible. It is essential in the process of assigning resources to new jobs while simultaneously optimising performance. Effective job scheduling is the only method by which it is possible to achieve the essential goals of any cloud computing architecture, including high performance, high profit, high utilisation, scalability, provision efficiency, and economy. This article gives a framework based on chaotic grey wolf optimization (CGWO

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Zhang, Runze, and Yujie Zhu. "Predicting the Mechanical Properties of Heat-Treated Woods Using Optimization-Algorithm-Based BPNN." Forests 14, no. 5 (2023): 935. http://dx.doi.org/10.3390/f14050935.

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This paper aims to enhance the accuracy of predicting the mechanical behavior of wood subjected to thermal modification using an improved dung beetle optimization (IDBO) model. The IDBO algorithm improves the original DBO algorithm via three main steps: (1) using piece-wise linear chaotic mapping (PWLCM) to generate the initial dung beetle species and increase its heterogeneity; (2) adopting an adaptive nonlinear decreasing producer ratio model to control the number of producers and boost the algorithm’s convergence rate; and (3) applying a dimensional learning-enhanced foraging (DLF) search s

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Wang, Erlei, Jiangying Xia, Jia Li, Xianke Sun, and Hao Li. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm." Energy 261 (December 2022): 125146. http://dx.doi.org/10.1016/j.energy.2022.125146.

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HADNI, Meryeme, Hassane HJIAJ, Mounir GOUIOUEZ, and Meryeme AMANE. "A novel perspective using Chaotic-Grey Wolf Optimization Algorithm for Arabic Feature Selection Problem." Procedia Computer Science 244 (2024): 416–24. http://dx.doi.org/10.1016/j.procs.2024.10.216.

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M, Janaki, Sarojini Balakrishnan, and Geethalakshmi S N. "Human Activity Recognition Using Chaotic Logistic Map Guided Grey Wolf Optimization with Decision Tree." International Journal of Electrical and Electronics Engineering 12, no. 1 (2025): 142–50. https://doi.org/10.14445/23488379/ijeee-v12i1p113.

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Lu, Xinyi, Yan Guan, Junyu Liu, Wenye Yang, Jiayin Sun, and Jing Dai. "Research on Real-Time Prediction Method of Photovoltaic Power Time Series Utilizing Improved Grey Wolf Optimization and Long Short-Term Memory Neural Network." Processes 12, no. 8 (2024): 1578. http://dx.doi.org/10.3390/pr12081578.

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This paper proposes a novel method for the real-time prediction of photovoltaic (PV) power output by integrating phase space reconstruction (PSR), improved grey wolf optimization (GWO), and long short-term memory (LSTM) neural networks. The proposed method consists of three main steps. First, historical data are denoised and features are extracted using singular spectrum analysis (SSA) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Second, improved grey wolf optimization (GWO) is employed to optimize the key parameters of phase space reconstruction (PSR) and

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Ibrahim, Rehab Ali, Mohamed Abd Elaziz, and Songfeng Lu. "Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization." Expert Systems with Applications 108 (October 2018): 1–27. http://dx.doi.org/10.1016/j.eswa.2018.04.028.

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Assiri, Adel Saad. "On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection." PLOS ONE 16, no. 1 (2021): e0242612. http://dx.doi.org/10.1371/journal.pone.0242612.

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Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal

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Assiri, Adel Saad. "On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection." PLOS ONE 16, no. 1 (2021): e0242612. http://dx.doi.org/10.1371/journal.pone.0242612.

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Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal

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Hu, Shanshan, Hui Liu, Yufei Feng, et al. "Tool Wear Prediction in Glass Fiber Reinforced Polymer Small-Hole Drilling Based on an Improved Circle Chaotic Mapping Grey Wolf Algorithm for BP Neural Network." Applied Sciences 13, no. 5 (2023): 2811. http://dx.doi.org/10.3390/app13052811.

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Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool wear experiments of GFRP, the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were found to be the main factors in drilling damage and tool wear. Using those main factors as the input layer, a tool wear and delamination factors prediction model was es

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Belbachir, N., M. Zellagui, S. Settoul, C. Z. El-Bayeh, and B. Bekkouche. "Simultaneous optimal integration of photovoltaic distributed generation and battery energy storage system in active distribution network using chaotic grey wolf optimization." Electrical Engineering & Electromechanics, no. 3 (June 23, 2021): 52–61. http://dx.doi.org/10.20998/2074-272x.2021.3.09.

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Goal. The integration of photovoltaic distributed generations in the active distribution network has raised quickly due to their importance in delivering clean energy, hence, participating in solving various problems as climate change and pollution. Adding the battery energy storage systems would be considered as one of the best choices in giving solutions to the mentioned issues due to its characteristics of quick charging and discharging, managing the quality of power, and fulfilling the peak of energy demand. The novelty of the proposed work is the development of new multi-objective functio

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Ardiansyah, Sri Handayaningsih, and Deva Fathurrizki. "Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 3 (2025): 671–80. https://doi.org/10.25126/jtiik.2025128901.

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COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GW

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Ardiansyah, Sri Handayaningsih, and Deva Fathurrizki. "Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 3 (2025): 671–80. https://doi.org/10.25126/jtiik.20258901.

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Abstract:

COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GW

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Chen, Wenwei, Lisang Liu, Liwei Zhang, Zhihui Lin, Jian Chen, and Dongwei He. "Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach." Applied Sciences 15, no. 7 (2025): 3999. https://doi.org/10.3390/app15073999.

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To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm’s global and local search capabilities.

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Cai, Zhihao, Jiang Lou, Jiang Zhao, Kun Wu, Ningjun Liu, and Ying Xun Wang. "Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control." Mechanical Systems and Signal Processing 128 (August 2019): 636–54. http://dx.doi.org/10.1016/j.ymssp.2019.03.035.

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WANG, Yingxun, Tian ZHANG, Zhihao CAI, Jiang ZHAO, and Kun WU. "Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy." Chinese Journal of Aeronautics 33, no. 11 (2020): 2877–97. http://dx.doi.org/10.1016/j.cja.2020.04.028.

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N., Belbachir, Zellagui M., Settoul S., Z. El-Bayeh C., and Bekkouche B. "Simultaneous optimal integration of photovoltaic distributed generation and battery energy storage system in active distribution network using chaotic grey wolf optimization." Electrical Engineering & Electromechanics, no. 3 (June 25, 2021): 52–61. https://doi.org/10.20998/2074-272X.2021.3.09.

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<strong><em>Goal. </em></strong><em>The integration of photovoltaic distributed generations in the active distribution network has raised quickly due to their importance in delivering clean energy, hence, participating in solving various problems as climate change and pollution. Adding the battery energy storage systems would be considered as one of the best choices in giving solutions to the mentioned issues due to its characteristics of quick charging and discharging, managing the quality of power, and fulfilling the peak of energy demand. <strong>The novelty </strong>of the p

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Asha, C. S., Shyam Lal, Varadraj Prabhu Gurupur, and P. U. Prakash Saxena. "Multi-Modal Medical Image Fusion With Adaptive Weighted Combination of NSST Bands Using Chaotic Grey Wolf Optimization." IEEE Access 7 (2019): 40782–96. http://dx.doi.org/10.1109/access.2019.2908076.

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Shaik, Khaja Shareef, Naga Siva Kumar Thumboor, Siva Prasad Veluru, Naga Jagadesh Bommagani, Dorababu Sudarsa, and Ganesh Karthik Muppagowni. "Enhanced SVM Model with Orthogonal Learning Chaotic Grey Wolf Optimization for Cybersecurity Intrusion Detection in Agriculture 4.0." International Journal of Safety and Security Engineering 13, no. 3 (2023): 509–17. http://dx.doi.org/10.18280/ijsse.130313.

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A. Hameedi, Balsam, Muntaha A. Hatem, and Jamal N. Hasoon. "Dynamic Key Generation Using GWO for IoT System." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 819. http://dx.doi.org/10.62527/joiv.8.2.2761.

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One well-known technological advancement that significantly impacts many things is the Internet of Things (IoT). These include connectivity, work, healthcare, and the economy. IoT can improve life in many situations, including classrooms and smart cities, through work automation, increased output, and decreased worry. However, cyberattacks and other risks significantly impact intelligent Internet of Things applications. Key generation is essential in information security and the various applications that use a distributed system, networks, or Internet of Things (IoT) systems. Several algorithm

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Liu, Changbao, and Yuexia Zhang. "5G Reconfigurable Intelligent Surface TDOA Localization Algorithm." Electronics 13, no. 12 (2024): 2409. http://dx.doi.org/10.3390/electronics13122409.

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In everyday life, 5G-based localization technology is commonly used, but non-line-of-sight (NLOS) environments can block the propagation of the localization signal, thus preventing localization. In order to solve this problem, this paper proposes a reconfigurable intelligent surface non-line-of-sight time difference of arrival (TDOA) localization (RNTL) algorithm. Firstly, a model of a reflective-surface-based intelligent localization (RBP) system is constructed, which utilizes multiple RISs deployed in the air to reflect signals. Secondly, in order to reduce the localization error, this paper

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