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Relevant bibliographies by topics / Grey Wolf Optimizer

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Academic literature on the topic 'Grey Wolf Optimizer'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 June 2025

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Journal articles on the topic "Grey Wolf Optimizer"

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Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey Wolf Optimizer." Advances in Engineering Software 69 (March 2014): 46–61. http://dx.doi.org/10.1016/j.advengsoft.2013.12.007.

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Kumar, Shailender, Kamran Sayeed, Anubhav Chhikara, and Durin Dai. "Forecasting Energy Demand of India Using Integrated Grey Wolf Optimizer." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3605–12. http://dx.doi.org/10.1166/jctn.2020.9239.

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In this paper we provide a new methodology for estimating the future primary energy demands of India. Firstly, we propose a new algorithm known as Integrated Grey wolf Optimizer. This new algorithm is an improvement over Grey wolf optimizer to deal with multimodal functions. Economic factors such as GDP (Gross Domestic Product), Population, Coal production and Petroleum production are used as mathematical parameters for our objective function. The coefficients of this two-form model (i.e., Linear and Quadratic) are optimized using the new Integrated Grey wolf optimizer. The highlight of this extract is the new Integrated version of grey wolf optimizer, which improves the exploration capability of the algorithm to deal with local minima stagnation. The results of this modified version are better than traditional Grey wolf optimizer and provides better accuracy and less errors. The last 14 years of historical information of India are used as datasets for the respective parameters. Coefficients obtained after the optimization are used for forecasting in three different cases which are Rapid (7.5% rise in GDP), Moderate (6.5%) and (5.5%) Slow growth of country.

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Zhang, Xiaoqing, Yuye Zhang, and Zhengfeng Ming. "Improved dynamic grey wolf optimizer." Frontiers of Information Technology & Electronic Engineering 22, no. 6 (2021): 877–90. http://dx.doi.org/10.1631/fitee.2000191.

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Chen, Yingyu, Shenhua Yang, Yongfeng Suo, and Minjie Zheng. "Ship Track Prediction Based on DLGWO-SVR." Scientific Programming 2021 (September 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/9085617.

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To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.

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Kitonyi, Peter Mule, and Davies Rene Segera. "Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection." BioMed Research International 2021 (August 28, 2021): 1–33. http://dx.doi.org/10.1155/2021/2555622.

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Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning repository, on metrics such as accuracy, size of feature subsets, F -measure, accuracy, precision, and sensitivity. The proposed optimizer outperformed the three other optimizers in 3 of the 6 datasets in average metrics. The proposed optimizer showed promise in its capability to balance the two objectives in feature selection and could be further enhanced.

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Hu, Pin, Siyi Chen, Huixian Huang, Guangyan Zhang, and Lian Liu. "Improved Alpha-Guided Grey Wolf Optimizer." IEEE Access 7 (2019): 5421–37. http://dx.doi.org/10.1109/access.2018.2889816.

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Mat Yasin, Zuhaila, Nur Ashida Salim, Nur Fadilah Ab Aziz, Hasmaini Mohamad, and Norfishah Ab Wahab. "Prediction of solar irradiance using grey wolf Optimizer-Least-Square support vector machine." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (2020): 10. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp10-17.

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<span>Prediction of solar irradiance is important for minimizing energy costs and providing high power quality in a photovoltaic (PV) system. This paper proposes a new technique for prediction of hourly-ahead solar irradiance namely Grey Wolf Optimizer- Least-Square Support Vector Machine (GWO-LSSVM). Least Squares Support Vector Machine (LSSVM) has strong ability to learn a complex nonlinear problems. In GWO-LSSVM, the parameters of LSSVM are optimized using Grey Wolf Optimizer (GWO). GWO algorithm is derived based on the hierarchy of leadership and the grey wolf hunting mechanism in nature. The main step of the grey wolf hunting mechanism are hunting, searching, encircling, and attacking the prey. The model has four input vectors: time, relative humidity, wind speed and ambient temperature. Mean Absolute Performance Error (MAPE) is used to measure the prediction performance. Comparative study also carried out using LSSVM and Particle Swarm Optimizer-Least Square Support Vector Machine (PSO-LSSVM). The results showed that GWO-LSSVM predicts more accurate than other techniques. </span>

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Alzaghoul, Esra F., and Sandi N. Fakhouri. "Collaborative Strategy for Grey Wolf Optimization Algorithm." Modern Applied Science 12, no. 7 (2018): 73. http://dx.doi.org/10.5539/mas.v12n7p73.

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Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.

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Gupta, Shubham, and Kusum Deep. "A novel Random Walk Grey Wolf Optimizer." Swarm and Evolutionary Computation 44 (February 2019): 101–12. http://dx.doi.org/10.1016/j.swevo.2018.01.001.

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Mohammedi, Ridha Djamel, Rabie Zine, Mustafa Mosbah, and Salem Arif. "Optimum Network Reconfiguration using Grey Wolf Optimizer." TELKOMNIKA (Telecommunication Computing Electronics and Control) 16, no. 5 (2018): 2428. http://dx.doi.org/10.12928/telkomnika.v16i5.10271.

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Dissertations / Theses on the topic "Grey Wolf Optimizer"

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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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Bhandare, Ashray Sadashiv. "Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural Networks." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273210921513.

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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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Javidsharifi, M., T. Niknam, J. Aghaei, Geev Mokryani, and P. Papadopoulos. "Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm." 2018. http://hdl.handle.net/10454/16610.

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Yes<br>Investigation of the environmental/economic optimal operation management of a microgrid (MG) as a case study for applying a novel modified multi-objective grey wolf optimizer (MMOGWO) algorithm is presented in this paper. MGs can be considered as a fundamental solution in order for distributed generators’ (DGs) management in future smart grids. In the multi-objective problems, since the objective functions are conflict, the best compromised solution should be extracted through an efficient approach. Accordingly, a proper method is applied for exploring the best compromised solution. Additionally, a novel distance-based method is proposed to control the size of the repository within an aimed limit which leads to a fast and precise convergence along with a well-distributed Pareto optimal front. The proposed method is implemented in a typical grid-connected MG with non-dispatchable units including renewable energy sources (RESs), along with a hybrid power source (micro-turbine, fuel-cell and battery) as dispatchable units, to accumulate excess energy or to equalize power mismatch, by optimal scheduling of DGs and the power exchange between the utility grid and storage system. The efficiency of the suggested algorithm in satisfying the load and optimizing the objective functions is validated through comparison with different methods, including PSO and the original GWO.<br>Supported in part by Royal Academy of Engineering Distinguished Visiting Fellowship under Grant DVF1617\6\45

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Wei-HsinYen and 顏維信. "Enhanced Grey Wolf Optimizer based Multiple Object Grasping Poses for Home Service Robot." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/64688212675751809429.

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碩士<br>國立成功大學<br>電機工程學系<br>104<br>The thesis proposes an Enhanced Grey Wolf Optimizer (EGWO) to learn multiple grasping poses of unknown objects for a home service robot. For accomplishing this task, a 3D model of an object should be established at first. The depth information obtained from Kinect is converted to 3D points in every frame and is matched by Iterative Closest Point (ICP) to track the pose of Kinect. The matched points are then integrated to a volumetric surface, and the result is presented by ray casting. However, the result of 3D object model is too complex to calculate grasping pose. So that a simplified method is proposed in this thesis. The original 3D object model is transferred to a triangle mesh firstly, and the triangle vertices are classified by three-stage nearest neighbor algorithm to find surfaces of the object. Therefore, the simplified surfaces can be constructed by the least square method and the object can be described by these simplified surfaces. After established a 3D object model, this thesis proposes Enhanced Grey Wolf Optimizer (EGWO) to learn multiple grasping poses. Due to the original Grey Wolf Optimizer (GWO) is highly corresponding with the origin of searching space, the performance will be influenced by the place of global optimal. The proposed EGWO solves the problem by eliminating the influence of the origin. In addition, it adds a position error term to maintain the good exploration and exploitation. The position error term is calculated by the difference between current and previous positions. If the difference is large, the algorithm forces more on exploration. Contrarily, the algorithm forces more on exploitation. Both the simulations and robotic experiments demonstrate that EGWO provides much better performance than GWO on learning multiple grasping poses and makes the home service robot successfully grasp unknown objects.

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HU, SHIH-YING, and 胡世穎. "Design and Implementation of MPPT Controller for Photovoltaic Power Generation with Grey Wolf Optimizer." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/42963042724151183949.

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碩士<br>國立臺南大學<br>電機工程學系碩博士班<br>105<br>In recent years, increasing numbers of photovoltaic (PV) generation system have entered the market. However with the current engineering technology, including solar panels, shading, circuit issues, it still causes a lot of energy losses in the generation process, resulting in direct economic benefits of the overall system that cannot competed with the traditional system. Thus, in this thesis, it is aimed to develop a novel circuit method to improve the efficiency of the photovoltaic generation operation. Resulting from its intrinsic characteristics, power generated by the solar panel leans on the operational condition at any given time, and the maximum power can be extracted from it varies accordingly. In fact, many maximum power point tracking methods exist in the literature. But those methods are reported not providing a faster tracking speed and accuracy under various environmental conditions. Therefore in this thesis, a new method based on the grey wolf optimization is proposed. This method utilizes the previous working duty cycles and their corresponding voltage and current data to define the instantaneous impedance value of solar cells. Then, by a progressive renewal of the iteration of grey wolf optimizer algorithm, the optimal MPP of solar panel can be found faster than the traditional. In order to assess the performance of this proposed method, it has been tested on the simple realization of system circuit. The preliminary results help consolidate the feasibility and practicability of the approach for the applications considered.

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Book chapters on the topic "Grey Wolf Optimizer"

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Ali, Ahmed F., and Mohamed A. Tawhid. "Grey Wolf Optimizer." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-16.

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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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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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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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Duarte, Daniel, P. B. de Moura Oliveira, and E. J. Solteiro Pires. "Entropy Based Grey Wolf Optimizer." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62362-3_29.

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Rebello, Gustavo, and Edimar José de Oliveira. "Modified Binary Grey Wolf Optimizer." In Springer Tracts in Nature-Inspired Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2133-1_7.

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Wadhwa, Ankita, and Manish Kumar Thakur. "Repulsion-Based Grey Wolf Optimizer." In Proceedings of International Conference on Artificial Intelligence and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4992-2_36.

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Meiwal, Priyanka, Harish Sharma, and Nirmala Sharma. "Neighbourhood-Inspired Grey Wolf Optimizer." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5077-5_11.

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Ali, Shahnawaz, Swati Jadon, and Ankush Sharma. "Randomized Neighbour Grey Wolf Optimizer." In Soft Computing for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1048-6_19.

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Ali, Ahmed F., and Mohamed A. Tawhid. "Grey Wolf Optimizer - Modifications and Applications." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422607-16.

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Conference papers on the topic "Grey Wolf Optimizer"

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Martin, Benoit, Julien Marot, and Salah Bourennane. "Improved Discrete Grey Wolf Optimizer." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8552925.

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Bohat, Vijay Kumar, K. V. Arya, and Shyam Singh Rajput. "Prey Phase based Grey Wolf Optimizer." In 2018 Conference on Information and Communication Technology (CICT). IEEE, 2018. http://dx.doi.org/10.1109/infocomtech.2018.8722428.

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Dadhich, Chitra, Ninnala Sharma, and Harish Sharma. "Howling mechanism based grey wolf optimizer." In 2017 International Conference on Computer, Communications and Electronics (Comptelix). IEEE, 2017. http://dx.doi.org/10.1109/comptelix.2017.8003991.

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Kohli, Suhani, Manika Kaushik, Kashish Chugh, and Avinash Chandra Pandey. "Levy inspired Enhanced Grey Wolf Optimizer." In 2019 Fifth International Conference on Image Information Processing (ICIIP). IEEE, 2019. http://dx.doi.org/10.1109/iciip47207.2019.8985722.

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Devi, G. S. K. Gayatri, and S. Krishnaveni. "Synthesis of CCAA using Grey Wolf Optimizer." In 2019 IEEE International Conference on Intelligent Systems and Green Technology (ICISGT). IEEE, 2019. http://dx.doi.org/10.1109/icisgt44072.2019.00035.

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Liang, Xu, Di Wang, and Ming Huang. "Improved Grey Wolf Optimizer and Their Applications." In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2019. http://dx.doi.org/10.1109/iccsnt47585.2019.8962504.

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Musa, Abubakar Ahmad, Sukairaj Hafiz Imam, Ankur Choudhary, and Arun Prakash Agrawal. "Parameter Estimation of Software Reliability Growth Models: A Comparison Between Grey Wolf Optimizer and Improved Grey Wolf Optimizer." In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377194.

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R, Karthikeyan. "Grey Wolf Optimizer algorithm-based unit commitment problem." In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2020. http://dx.doi.org/10.1109/i-smac49090.2020.9243458.

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Wong, L. I., M. H. Sulaiman, M. R. Mohamed, and M. S. Hong. "Grey Wolf Optimizer for solving economic dispatch problems." In 2014 IEEE International Conference on Power and Energy (PECon). IEEE, 2014. http://dx.doi.org/10.1109/pecon.2014.7062431.

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Dida, Hedifa, Fella Charif, and Abderrazak Benchabane. "Grey Wolf Optimizer for Multimodal Medical Image Registration." In 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS). IEEE, 2020. http://dx.doi.org/10.1109/icds50568.2020.9268771.

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