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Dynamic grey wolf optimization algorithm Based on Quasi-Opposition Learning 1

3rd International Conference on 3D Imaging Technologies - Multidimensional Signal Processing and Deep Learning (3DIT-MSP and DL)

作者： Wang, Tianlei Li, Junhui Liu, Renju Xu, Jinzhao Hao, Xiaoxi Kin, Kenneth Teo Tze Liang, Jiehong Wuyi Univ Fac Intelligent Mfg Jiangmen 529020 Guangdong Peoples R China Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China Wuyi Univ Sch Math & Comp Sci Jiangmen 529020 Guangdong Peoples R China Univ Malaysia Sabah Fac Engn Kota Kinabalu Sabah Malaysia Jinming Machinery Mfg Co LTD Shantou Peoples R China

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

Although the structure of grey wolf optimization algorithm (GWO) is simple and easy to understand, it has the disadvantage of unbalanced exploration and exploitation. To solve the problem, this paper puts forward a kind of learning strategy based on quasi-opposition learning and dynamic search strategy improved grey wolf optimization algorithm. That is verified on the nine benchmark test functions, the experimental results proved that the improved grey wolf optimization algorithm has better search performance, compared with the cuckoo search algorithm (CS) and particle swarm optimization (PSO) algorithm at the same time, by contrast, improved algorithm has the advantages of larger.

关键词： Swarm intelligence grey wolf optimization algorithm Quasi-opposition-based learning

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Antenna optimization Based on Quasi-Opposition grey wolf optimization algorithm 14

Antenna Optimization Based on Quasi-Opposition Grey Wolf Opt...

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14th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Zhu, Hao-Yun Li, Wei-Dong Zhao, Hai-Yan Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

ISBN: (纸本)9781665467520

Performance optimization of an antenna is a non-linear, multi-dimensional and complex scheme, which can be solved by applying intelligence algorithms. A Quasi-Opposition grey wolf optimization (QOGWO) algorithm is presented to improve the grey wolf optimization (GWO) algorithm. It is prone to global optimality, high precision for complex problems and a fast convergence rate at a later stage. A wideband dual-polarized magneto-electric dipole antenna is simulated as an example. Compared to the GWO algorithm, the antenna optimized by the improved algorithm has a wider bandwidth, verifying the feasibility of the algorithm in the field of antenna design optimization.

关键词： Antenna Quasi-Opposition grey wolf optimization algorithm

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Solving nonlinear differential equations using a hybrid method between the variational iteration method and grey wolf optimization algorithm

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2022年第2期25卷 573-585页

作者： Entesar, Ahmed Mahmood, Hiba Shuker Qasim, Omar Saber Univ Mosul Coll Comp Sci & Math Dept Math Mosul Iraq Univ Mosul Coll Educ Pure Sci Dept Math Mosul Iraq

Done in this manuscript, an analytical method, Variation Iteration Method (VIM) has been hybridized with an intelligent method the Gray wolf optimization (GWO) algorithm, by solving nonlinear Ordinary Differential Equations, where the results obtained from the analytical method have been improved by (GWO) algorithm. The results obtained by calculating the mean square error (MSE) and estimating the maximum absolute error (MAE) proved the efficiency of this method.

关键词： Variation iteration method grey wolf optimization algorithm Non-linear ordinary differentiation equations

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Application of grey wolf optimization algorithm in Tuning Controller Parameters of Hypersonic Vehicle 41

Application of Grey Wolf Optimization Algorithm in Tuning Co...

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第41届中国控制会议

作者： Can Tian Shaobo Ni Zhaolei Wang Yuan Zhang National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

There are many adjustable parameters in the control method of predictive sliding mode control(PSMC) law and linear extended state observer(LESO) for hypersonic vehicle speed and altitude,and the adjustable parameters influence each other on the control effect,and the speed and altitude channels of the aircraft are coupled with each other,so the controller parameters cannot be independently tuned.A method of automatically tuning controller parameters by using grey wolf optimization(GWO) algorithm is ***,the fitness function containing the desired control effect is designed,and then the appropriate number of search agent and iteration times are *** simulation results show that the control effect of the controller with GWO tuning parameters is obviously better than that of the controller with experience tuning parameters,and the expected control effect can be achieved under the condition of adding parameter deviation and actuator limitation.

关键词： predictive sliding mode control linear extended state observer grey wolf optimization algorithm Auto-tuning controller parameters

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Improved grey wolf optimization algorithm for Overcurrent Relays Coordination 9

Improved Grey Wolf Optimization Algorithm for Overcurrent Re...

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9th IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Jamal, Noor Zaihah Sulaiman, Mohd Herwan Aliman, Omar Mustaffa, Zuriani Mustafa, Mohd Wazir Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Fac Comp Syst & Software Engn Pekan 26600 Pahang Malaysia Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Johor Malaysia

ISBN: (纸本)9781538663219

Recently, nature inspired algorithms (NIA) have been implemented to various fields of optimization problem such as biomedical engineering, electrical engineering, computer science and etc. The achievement of NIA in solving optimization in these fields becoming the motivation to apply one of the NIA namely grey wolf Optimizer (GWO) into overcurrent relay coordination problem. However, the current state of GWO suffers lack of exploration problem. Hence, the improvement of GWO has been proposed in this paper to enhance the exploration of original GWO. The improvement of GWO (IGWO) is implemented in finding the the optimal value of the Time Multiplier Setting (TMS) and Plug Setting (PS) in order to minimize the primary relays' operating time at the near end fault. Comprehensive simulation studies have been performed to demonstrate the reliability and efficiency of the proposed modification technique compared to the original GWO. The generated results have confirmed the proposed IGWO is able to improve the objective function of the overcurrent relay coordination problem.

关键词： Time multiplier setting plug setting grey wolf optimization algorithm overcurrent relay coordination

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Hybrid Load Identification Model Based on grey wolf optimization algorithm 25

Hybrid Load Identification Model Based on Grey Wolf Optimiza...

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25th IEEE International Conference on Automation and Computing (IEEE ICAC)

作者： Lv Xinwei Ren Zhiren Tang Bo Liu Hui Yang Rui Wu Haiping Was Grp Co Ltd Changsha 410000 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

ISBN: (纸本)9781861376657

Non-intrusive load monitoring is important for the development of smart grids. In order to get the load status and power consumption information of each device, single classifiers such as the support vector machines, the MLP neural networks and the extreme learning machines are widely used to identify the appliances. But the single classifiers are faced with the risk of local optimum and overfitting. In order to improve the recognition accuracy of the single classic classifiers, a hybrid identification model based on grey wolf optimization algorithm is proposed in this paper. The experimental results based on the actual measured data verified that the recognition accuracy of the proposed method is significantly higher than that of the single classical classification models.

关键词： Non-intrusive load identification grey wolf optimization algorithm hybrid model

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A grey wolf optimization algorithm for Multi Strategy Network Coverage Based on Roulette Wheel 24

A Grey Wolf Optimization Algorithm for Multi Strategy Networ...

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11th International Conference on Wireless Communication and Sensor Networks (icWCSN)

作者： Ma, Qianli Qi, Xin Qian, Huimeng Cui, Zhiheng Shanxi Univ Sch Comp & Informat Technol Taiyuan Peoples R China

ISBN: (纸本)9798400709005

The key to wireless sensor network coverage lies in how to achieve full coverage of the region with as few sensor nodes as possible. The research on wireless sensor network coverage often faces problems such as low optimization accuracy, slow convergence speed, and susceptibility to premature convergence. To address these issues, A multi strategy network coverage grey wolf optimization algorithm (MSGWO) based on roulette wheel is proposed. Firstly, a mixed mapping of Tent and Circle maps is used to map the initial distribution of wireless sensor nodes, so that the initial distribution of sensor nodes is as evenly distributed as possible in the space. Secondly, as the iteration cycle changes, multi strategy position updates are performed on the gray wolves that meet the conditions in the grey wolf optimization algorithm (GWO) to improve the ability to jump out of local convergence. Finally, in the iterative optimization of the grey wolf optimization algorithm, a leadership strategy based on the roulette wheel method was used to expand the global search range of the algorithm. Simulation experiments have shown that MSGWO has higher coverage and less time consumption.

关键词： wireless sensor network coverage optimization grey wolf optimization algorithm coverage rate

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An adaptive elitism-based immigration for grey wolf optimization algorithm 2

An adaptive elitism-based immigration for grey wolf optimiza...

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International Conference on Digital Arts, Media and Technology (ICDAMT) - Digital Economy for Sustainable Growth

作者： Jitkongchuen, Duangjai Phaidang, Pongsak Pongtawevirat, Piyalak Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

ISBN: (纸本)9781509052103

This paper proposed the adaptive elitism-based immigration to improve the grey wolf optimization performance. The concept of elitism-based immigration is to generated immigrants and replaces it to the worst individuals in the population. The elite immigrants in our proposed are mutated before replace to the worst individuals and the parameter to control mutation ratio and elite immigrants ratio are adaptive. The performances have been evaluated by using 7 well-known benchmark functions and compared with the traditional grey wolf optimizer (GWO) algorithm, particle swarm optimization (PSO) and differential evolution (DE) algorithm. The experimental results showed that the proposed algorithm has ability to solving optimization problems.

关键词： Metaheuristic algorithm grey wolf optimization algorithm Elitism-base immigrants optimization problems

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Mobile robot navigation based on Fuzzy Cognitive Map optimized with grey wolf optimization algorithm used in Augmented Reality 5

Mobile robot navigation based on Fuzzy Cognitive Map optimiz...

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5th RSI International Conference on Robotics and Mechatronics (IcRoM)

作者： Malayjerdi, Ehsan Yaghoobi, Mahdi Kardan, Mohammad Islamic Azad Univ Mashhad Branch Dept Artificial Intelligence Mashhad Razavi Khorasan Iran Khayyam Univ Dept Comp Sci Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781538657034

this work presents a control technique for Mobile Robot Navigation using augmented reality (AR). This navigation technique is based on optimized Fuzzy Cognitive Map (FCM) and AR's Glyphs. AR's symbols are provided by the overhead camera. The patterns are made up of glyphs and a clear path. Six practical test are manipulated to examine the strength of optimizing FCM by a mobile robot for navigation with AR's symbols. The experiment examined the effectiveness of a grey wolf optimization algorithm (GWOA) in optimizing the FCM. Two practical experiments confirm that AR's Glyphs are an effective symbol for a robot to navigation in an unknown environment. A practical experiment reveals that a robot can use AR to manage its intended movement. Augmented reality, such as the Glyphs and a simplified map, are an effective tool for mobile robots to use in navigation in unknown environments. A prototype system is made to navigate the mobile robot by using AR and FCM.

关键词： Augmented reality Fuzzy Cognitive Map grey wolf optimization algorithm Mobile Robot Glyph

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Optimal Operation Strategy for Integrated Energy Systems Based on Improved grey wolf optimization algorithm 3

Optimal Operation Strategy for Integrated Energy Systems Bas...

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3rd International Conference on Power Systems and Electrical Technology, PSET 2024

作者： Jiang, Chongying Qin, Shaoji Lin, Kaidong Liu, Siliang Deng, Fengqiang Zhang, Jun South China University of Technology School of Electrical Power Guangzhou China

ISBN: (纸本)9798350353204

The optimization of the multi-energy storage configuration within the Integrated Energy System (IES) is achieved through the utilization of an Improved grey wolf optimization algorithm (IGWO), which enhances economic efficiency, reliability, and environmental sustainability. An IES model incorporating wind and photovoltaic generation along with various storage devices has been established, alongside the development of a multi-objective model focused on minimizing costs and emissions. The introduction of a Gaussian mutation strategy within the improved algorithm bolsters exploratory capabilities and enhances population diversity. Furthermore, an entropy-weight method has been employed to automatically determine the weights of evaluation criteria, ensuring the objectivity and rigor of decision-making. Validation through case studies has confirmed the method's effectiveness in enhancing the cost-efficiency and environmental performance of the IES, showcasing its capability to manage energy flows effectively, reduce costs, and minimize environmental impacts while maintaining system performance. © 2024 IEEE.

关键词： Entropy Weight Method grey wolf optimization algorithm Integrated Energy System Multi-energy Storage Multi-objective optimization

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