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Relevant bibliographies by topics / Chaotic grey wolf optimization

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Academic literature on the topic 'Chaotic grey wolf optimization'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Chaotic grey wolf optimization"

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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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Dissertations / Theses on the topic "Chaotic grey wolf optimization"

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Lakshminarayanan, Srivathsan. "Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438102173.

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Yarlagadda, Rahul Rama Swamy. "Inverse Modeling: Theory and Engineering Examples." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1449724104.

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Oliveira, Clemar Trentin. "Otimização de um trocador de calor casco e tubos utilizando o algoritmo lobo cinzento." Universidade do Vale do Rio dos Sinos, 2018. http://www.repositorio.jesuita.org.br/handle/UNISINOS/7648.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2019-03-13T13:59:33Z No. of bitstreams: 1 Clemar Trentin Oliveira_.pdf: 4154239 bytes, checksum: 0abf1d28fa69ea97d15e72a7624615f5 (MD5)<br>Made available in DSpace on 2019-03-13T13:59:33Z (GMT). No. of bitstreams: 1 Clemar Trentin Oliveira_.pdf: 4154239 bytes, checksum: 0abf1d28fa69ea97d15e72a7624615f5 (MD5) Previous issue date: 2018-12-11<br>Nenhuma<br>Neste trabalho, desenvolve-se uma nova abordagem de otimização do projeto de um trocador de calor casco e tubos. O algoritmo Otimizador por Lobo Cinzento (GWO) é aplicado para minimiz

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Elahi, Behin. "Integrated Optimization Models and Strategies for Green Supply Chain Planning." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1467266039.

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El-Bidairi, KSN. "Fuzzy-grey wolf optimization for energy storage sizing and power management in microgrids." Thesis, 2019. https://eprints.utas.edu.au/34066/1/El_Bidairi__whole_thesis.pdf.

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Escalating fossil fuel prices, pressure of more stringent environmental regulations, and deregulation in the electricity market provide opportunities and motives for renewable energy sources (RESs), such as wind, solar, tidal and wave energy to be integrated into existing power grids. Nevertheless, due to the distributed nature of RESs, the traditional centralized power system architecture, as well as control mechanisms, are not adequate to support the integration of renewable energy systems. Therefore, the concept of a microgrid emerged, where a group of distributed energy sources and loads,

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Chang, Pei-Cheng, and 張沛承. "A Modified Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/55p3tc.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>107<br>Partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a multiple-peak power-voltage curve. The common MHAs, such as particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO) and cuckoo search (CS) have been used for maximum power point tracking (MPPT). Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been u

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TRINH, BUI VAN, and BUI VAN TRINH. "A Hybrid Grey Wolf Optimization Algorithm using Robust Learning Mechanism for Large Scale Economic Load Dispatch with Valve-Point Effects." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/74cr6r.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>107<br>This research proposed a new hybrid algorithm of grey wolf optimization (GWO) integrated with robust learning mechanism to solve the large scale economic load dispatch (ELD) problem. The robust learning grey wolf optimization (RLGWO) algorithm imitate the hunting behavior and social hierarchy of grey wolves in nature and reinforced by robust tolerant based adjust searching direction and opposite based learning. This technique could effectively prevent search agents trapping into local optimum but also generate potential candidate to obtain feasible solutions.

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Book chapters on the topic "Chaotic grey wolf optimization"

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Kakkar, Mohit Kumar, Jasdev Bhatti, and Gourav Gupta. "Reliability Optimization of an Industrial Model Using the Chaotic Grey Wolf Optimization Algorithm." In Manufacturing Technologies and Production Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003367161-29.

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Mishra, Krishn Kumar. "Grey Wolf Optimization." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-6.

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Badar, Altaf Q. H. "Grey Wolf Optimizer." In Evolutionary Optimization Algorithms. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-8.

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Okwu, Modestus O., and Lagouge K. Tartibu. "Grey Wolf Optimizer." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_5.

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Rezaei, Hossein, Omid Bozorg-Haddad, and Xuefeng Chu. "Grey Wolf Optimization (GWO) Algorithm." In Advanced Optimization by Nature-Inspired Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_9.

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Mirjalili, Seyedali, and Jin Song Dong. "Multi-objective Grey Wolf Optimizer." In Multi-Objective Optimization using Artificial Intelligence Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24835-2_5.

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Kouziokas, Georgios N. "Grey Wolf, Whale and Grasshopper Optimization." In Swarm Intelligence and Evolutionary Computation. CRC Press, 2023. http://dx.doi.org/10.1201/9781003247746-7.

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Hamidia, Fethia, and Amel Abbadi. "MPPT Based On Grey Wolf Optimization." In Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92038-8_6.

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Das, Debashish, Ali Safaa Sadiq, and Seyedali Mirjalili. "Grey Wolf Optimizer: Foundations and Mathematical Models." In Engineering Optimization: Methods and Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3849-9_6.

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Upadhyay, Bhavik D., Sunil S. Sonigra, and Sachin D. Daxini. "Structural design optimization using grey wolf optimizer." In Challenges and Opportunities in Industrial and Mechanical Engineering: A Progressive Research Outlook. CRC Press, 2024. http://dx.doi.org/10.1201/9781032713229-50.

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Conference papers on the topic "Chaotic grey wolf optimization"

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Kumar, Santosh, Jyoti Bisht, and Tarun Sarawgi. "Multi-Criteria Adaptive Websites using Grey Wolf Optimization." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717589.

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Gupta, Megha, Abhinav Tomar, and Vibhor Kant. "Leveraging Grey Wolf Optimization for Multi-Criteria Recommender Systems." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752214.

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Lou, Tai-Shan, Haowei Fan, Zhendong He, and Guoqiang Ding. "Improved Grey Wolf Optimization Algorithm for UAV Path Planning." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865352.

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Abu-Shareha, Ahmad Adel, Mosleh M. Abualhaj, Ali AL-ALLAWEE, Alhamza Munther, and Mohammed Anbar. "Enhancing Malware Detection with Firefly and Grey Wolf Optimization Algorithms." In 2024 11th International Conference on Electrical and Electronics Engineering (ICEEE). IEEE, 2024. https://doi.org/10.1109/iceee62185.2024.10779310.

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Kori, Gururaj S., Mahabaleshwar S. Kakkasageri, Vijaykumar Hiremath, Poornima M. Chanal, Rajani S. Pujar, and Vinayak A. Telsang. "Cognitive Swarm Drones Attack Model: A Grey Wolf Optimization Approach." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677076.

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Yulistiani, Risma, and Felix Indra Kurniadi. "Feature Selection Using Grey Wolf Optimization for Fetal Health Classification." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10750950.

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Abdulsattar, Nejood F., Hassan Mohammed Abed, Amit Gangopadhyay, Mohammed I. Habelalmateen, Fatima Hashim Abbas, and Rusul Lsmael Hadi. "Energy Consumption Modeling and Grey Wolf Optimization for Vehicular Communication." In 2024 Asian Conference on Communication and Networks (ASIANComNet). IEEE, 2024. https://doi.org/10.1109/asiancomnet63184.2024.10811028.

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Sirhan, Najem, and Manel Martinez-Ramon. "Optimization of Multi-Hop Wireless Routing with Grey Wolf Optimizer." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013180.

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Fadhil, Heba Mohammed, Mohamed Baqer Ghazi, and Basma Sinan. "Enhancing Feature Selection with a Hybrid Grey Wolf Optimization Algorithm." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013680.

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Fadhil, Heba Mohammed, Mohammed Najm Abdullah, and Mohammed Issam Younis. "A Hybrid Grey Wolf-Whale Optimization Model for Feature Selection." In 2025 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2025. https://doi.org/10.1109/csase63707.2025.11054023.

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