Microservice combination optimisation based on improved gray wolf algorithm by Jingjing Hu, Xiaojun Xu, Jin Hao, Xiuqi Yang, Kefan Qiu, Yuanzhang Li

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Improved grey wolf optimization algorithm based service function chain mapping algorithm by Yue ZHANG, Junnan ZHANG, Xiaochun WU, Chen HONG, Jingjing ZHOU

“…With the rise of new Internet applications such as the industrial Internet, the Internet of vehicles, and the metaverse, the network’s requirements for low latency, reliability, security, and certainty are facing severe challenges.In the process of virtual network deployment, when using network function virtualization technology, there were problems such as low service function chain mapping efficiency and high deployment resource overhead.The node activation cost and instantiation cost was jointly considered, an integer linear programming model with the optimization goal of minimizing the average deployment network cost was established, and an improved grey wolf optimization service function chain mapping (IMGWO-SFCM) algorithm was proposed.Three strategies: mapping scheme search based on acyclic KSP algorithm, mapping scheme coding and improvement based on reverse learning and nonlinear convergence were added to the standard grey wolf optimization algorithm to form this algorithm.The global search and local search capabilities were well balanced and the service function chain mapping scheme was quickly determined by IMGWO-SFCM.Compared with the comparison algorithm, IMGWO-SFCM reduces the average deployment network cost by 11.86% while ensuring a higher service function chain request acceptance rate.…”
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Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm. by Xiaotong Bai, Yuefeng Zheng, Yang Lu, Yongtao Shi

“…In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). …”
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Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance by Narinder Singh, S. B. Singh

“…A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). …”
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Coverage Optimization Technology of Power Pipe Gallery Based on Improved Gray Wolf Algorithm by Cheng ZHONG, Di ZHAI, Yang LU, Xiaobo LIU, Xinru WANG, Xiongwen ZHAO

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Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm by Yongjin Lu, Kai Li, Rui Lin, Yunlong Wang, Hairong Han

“…The improved algorithm has better global search ability, higher solution stability, and faster convergence speed than the standard grey wolf optimization algorithm. …”
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Improved gray wolf harris hawk algorithm based feature selection for sentiment analysis by Tamara Amjad Al-Qablan, Mohd Halim Mohd Noor, Mohammed Azmi Al-Betar, Ahamad Tajudin Khader

“…Efficient sentiment feature selection (FS) is crucial for reducing data dimensionality and isolating relevant features to improve results. This study aims to enhance FS performance by addressing the population diversity issues in the Adaptive β Binary Gray Wolf Optimization (Aβ-BGWO) algorithm, which struggles to escape local optima. …”
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Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning by Shijin Li, Fucai Wang

“…Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. …”
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Research on DV-Hop location algorithm based on range correction and improved gray wolf optimizer by Xiaoqiang ZHAO, Shuai WU, Chuanyi GAO, Ning LI, Bodong LI, Xiaoyong YANG

“…Node location is an important problem in wireless sensor network.Although the location algorithm based on distance measurement has small positioning error, it has many limitations when applied to outdoor environments.Therefore, based on the original distance vector-hop (DV-Hop) algorithm, received signal strength indication (RSSI) technology and the minimum mean square error (MMSE) criterion to modify the algorithm’s ranging process were introduced, and the improved gray wolf optimizer was used to optimize the process of determining the coordinates of unknown nodes.Simulation results show that, compared with the original DV-Hop algorithm and IPDV-Hop algorithm, the average location error rate of the IGDV-Hop algorithm under the initial parameters was reduced by 28% and 17% respectively, and the location effect was significantly improved.…”
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Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization by Jomana Yousef Khaseeb, Arabi Keshk, Anas Youssef

“…Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. …”
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Point cloud registration based on surface feature extraction and an improved Grey Wolf Optimization algorithm by Zimei Tu, Yichen Xie, Jinhua Jiang, Qin Qin

“…The extracted feature points serve as the initial values for the improved gray wolf optimization algorithm, which is employed to refine the registration results. …”
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Improved Grey Wolf Algorithm: A Method for UAV Path Planning by Xingyu Zhou, Guoqing Shi, Jiandong Zhang

“…The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. …”
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Image Segmentation Based on the Optimized K-Means Algorithm with the Improved Hybrid Grey Wolf Optimization: Application in Ore Particle Size Detection by Xinyi Chai, Zijun Wu, Wei Li, Haowei Fan, Xinyang Sun, Jing Xu

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An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans by Yanzhao Gong, Richard Amankwa Adjei, Guocheng Tao, Yitao Zeng, Chengwei Fan

“…This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. …”
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Improved grey wolf optimizer for optimal reactive power dispatch with integration of wind and solar energy by F. Laouafi

“…The aim of this paper is to present a new improved grey wolf optimizer (IGWO) to solve the optimal reactive power dispatch (ORPD) problem with and without penetration of renewable energy resources (RERs). …”
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Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach by Wenwei Chen, Lisang Liu, Liwei Zhang, Zhihui Lin, Jian Chen, Dongwei He

“…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. …”
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Optimizing high-speed train tracking intervals with an improved multi-objective grey wolf by Lin Yue, Meng Wang, Peng Wang, Jinchao Mu

“…To achieve multi-objective dynamic optimization, a novel train tracking operation calculation method is proposed, utilizing the improved grey wolf optimization algorithm (MOGWO). …”
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Magnetic targets positioning method based on multi-strategy improved Grey Wolf optimizer by Binjie Lu, Zongji Li, Xiaobing Zhang

“…Therefore, a Multi-Strategy Improved Grey Wolf Optimizer (MSIGWO) algorithm has been proposed to enhance the accuracy of magnetic target state estimation. …”
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Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization by Muhammad Suhail Shaikh, Chang Wang, Senlin Xie, Gengzhong Zheng, Xiaoqing Dong, Shuwei Qiu, Mohd Ashraf Ahmad, Saurav Raj

“…The Grey Wolf Optimizer (GWO) is enhanced using a chaotic map, improving its ability to find the best solutions and achieve faster convergence, resulting in the ICGWO algorithm. …”
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Optimization on construction machinery considering sequence-dependent setup times and personnel fatigue based on the improved gray wolf and whale algorithm. by Dawei Wang, Bo Gao, Lei Zhang

“…An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”
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