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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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Fault Diagnosis Of Power Transformer Based On Extreme Learning Machine Optimized By Improved grey wolf optimization algorithm

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淡江理工学刊 2024年第4期27卷 2367-2374页

作者： Yong Xu Xiaojuan Lu Yuhang Zhu Jiawei Wei Dan Liu Jianchong Bai

For power transformers, the gas content in oil is used as the fault input feature quantity, and the accuracy of diagnosis results is not satisfactory. The problem of low accuracy of optimized extreme learning machine (ELM) of grey wolf optimization (GWO) algorithm is proposed, and a hybrid intelligent fault diagnosis method based on random forest and improved optimized extreme learning machine of grey wolf optimization algorithm is proposed. Firstly, the importance of the candidate gas ratios is score by random forest and reassembled into five groups of feature parameters in order of importance from highest to lowest and used as the input feature quantity of the model. Secondly, the extreme learning machine is optimized to randomly generate weights and thresholds using the improved grey wolf optimization algorithm to improve the prediction accuracy of the model. Finally, the simulation experiments and comparative test analysis show that the fault diagnosis model has particular effectiveness in transformer fault diagnosis.

关键词： Fault diagnosis Extreme learning machine Random forest grey wolf optimization algorithm Power transformer

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A local opposition-learning golden-sine grey wolf optimization algorithm for feature selection in data classification

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APPLIED SOFT COMPUTING 2023年 142卷

作者： Li, Zhang Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Jiangsu Peoples R China

The classification problem is an important research topic in machine learning and data mining. Feature selection can remove irrelevant and redundant features and improve classification accuracy. The traditional grey wolf algorithm (GWO) has the defects of low convergence efficiency and easy to falls into local extremes in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. This paper proposes a binary version of the local opposing learning golden sine grey wolf optimization algorithm (OGGWO). First, the OGGWO algorithm uses local opposing learning mapping to initialize the positions of individual grey wolves to enrich population diversity and improve convergence speed. Secondly, mix the golden sine algorithm and the grey wolf optimization algorithm to control the direction and distance of & alpha;wolves by using the golden mean coefficient to improve the autonomous search ability of individual grey wolves and avoid the algorithm from falling into the local optimum. Finally, the updated grey wolf position is binary converted by pre-setting the threshold value to reduce the feature subset's size and improve the classification effect. To verify the effectiveness of the OGGWO algorithm, 18 international standard datasets were selected, and compare the OGGWO algorithm with the improved grey wolf algorithm and the popular metaheuristic algorithm for the fitness value comparison test and the simulation comparison experiment for classification accuracy. The results show that: (1) the OGGWO algorithm has good convergence and high search accuracy on all 18 test data;(2) the improved strategy of the OGGWO algorithm can effectively improve the classification accuracy and reduce the number of selected features compared with the traditional GWO algorithm in the classification accuracy simulation. The experimental results show that the superiority and robustness of the OGGWO algorithm in feature selection are verified.& COPY;

关键词： Classification Feature selection grey wolf optimization algorithm Golden sine algorithm Local opposition-learning

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Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Liu, Xinyu Li, Guangquan Yang, Haoyuan Zhang, Nianru Wang, Longfei Shao, Peng Jiangxi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Peoples R China

Unmanned aerial vehicle (UAV) trajectory planning plays an essential role in agricultural production and biological control. To solve the agricultural UAV trajectory planning problem, a multi-mechanism collaborative improved grey wolf optimization algorithm (NAS-GWO) is proposed. In NAS-GWO, the evolutionary boundary constraint processing mechanism is introduced to update the position of the grey wolf individuals that cross the boundary in time to retain the position information of the optimal individuals to the largest degree to enhance the search accuracy of the algorithm. Then, the Gaussian mutation strategy and spiral function are used as perturbation mechanisms to help the algorithm jump out of the local optimum in time to strengthen the exploitation capability of NAS-GWO. Meanwhile, the improved Sigmoid function is used as a nonlinear convergence factor for balancing the exploitation and exploration of the NAS-GWO. By comparing NAS-GWO with ten advanced metaheuristic algorithms on 20 CEC2017 benchmark functions, the experimental results show that the NAS-GWO algorithm has superior merit seeking and robustness. Moreover, the agricultural UAV trajectory planning problem is solved using NAS-GWO. The experimental results show that the NAS-GWO algorithm plans a more viable and stable trajectory path in four different scale missions, while the most important is that it requires less cost. Among them, the algorithm reduces 27.93%, 38.15%, 32.32%, 34.11%, 10.63%, and 13.48% on average in cost function values compared to Particle Swarm optimization (PSO), Whale optimization algorithm (WOA), Aquila Optimizer (AO), Differential Evolution (DE), Dung Beetle Optimizer (DBO), and grey wolf optimization algorithm (GWO), thus proving the effectiveness and significance of NAS-GWO in the agricultural UAV trajectory path planning problem.

关键词： Agricultural UAV Trajectory planning grey wolf optimization algorithm Multiple mechanisms

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Short-term hydrothermal scheduling using grey wolf optimization algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 225卷

作者： Swain, Rajkishore Mishra, Umesh Chandra Govt Coll Engn Kalahandi Dept Elect Engn Bhawanipatna India Konark Inst Sci & Technol Dept Elect Engn Bhubaneswar India

This paper presents grey wolf optimization algorithm (GWO) for solving economic analysis of hydrothermal systems (HTS). The mathematical configuration of hydrothermal systems is considered a highly non-linear and complex problem. The intention of the HTS is to evaluate the optimal power allocation during a certain interval of time so as to minimize the total generation cost. The various constraints like the cascading nature of hydro plants, water transport delay, degree of power, water dispense limits, tank storage limits, hydraulic durability constraints, and starting and ending reservoir container limits are fully incorporated in the present work. The presented approach is then benchmarked on three well-known hydrothermal systems and the results are verified against different meta-heuristic techniques like Turbulent Water Flow optimization (TWFO), Crisscross optimization (CSO), Red Fox Optimizer (RFO), Remora optimization algorithm (ROA), Harris Hawks optimization (HHO) Monarch Butterfly optimization (MBO), Student Psychology-Based optimization (SPBO) and improved Gravitational Search algorithm (IGSA). The generation scheduling and minimum fuel cost of the thermal system are the main characteristics of the suggested approach, which gives competitive results with less computational burden. The fuel cost of a thermal plant with four cascaded hydro plants is 910,961.32 $/h, which is less than the fuel cost as reported by other existing evolutionary techniques compared in the simulation results. Further, the computational time of the purposed approach is found to be substantially less compared to existing algorithms. The statistical comparison between the GWO technique and the modified differential evolution (MDE) approach for four hydro plants and a thermal plant show that the MDE method has a higher mean cost than the GWO method. The standard deviation of the GWO technique is 5.4450 e + 03, whereas the standard deviation of the MDE approach is 9.2980 e + 03. The mea

关键词： grey wolf optimization algorithm Encircling prey Hydrothermal scheduling Cascaded reservoirs optimization

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Improved grey wolf optimization algorithm Based on Local Derivative Inertia Weights 23

Improved Grey Wolf Optimization Algorithm Based on Local Der...

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Proceedings of the 7th International Conference on algorithms, Computing and Systems

作者： Guo Chen School of Mathematics and Statistics Guangdong University of Foreign Studies China

ISBN: (纸本)9798400709098

Under the background of the development of big data and Internet economy, the effective prediction of Consumer Confidence Index (CCI) can ensure the formulation of relevant policies. In this study, to solve the problem that the grey wolf optimizer (GWO) algorithm has low optimization accuracy and slow convergence speed, the Improved grey wolf optimization algorithm Based on Local Derivative Inertia Weights (LGWO) is proposed. LGWO is a nonlinear inertia weight of derivative local formula based on information entropy and an improved grey wolf optimization algorithm that updates the position of population self-adaptively, which improves the optimization efficiency of GWO. By comparing the optimization performance of LGWO with other classical swarm intelligence algorithms through 7 typical test functions, it is confirmed that the convergence speed and solution accuracy of LGWO proposed in this paper are greatly improved compared with other classical algorithms. Subsequently, this paper establishes LGWO-BP model to predict CCI, and the simulation analysis results show that LGWO-BP model has higher prediction accuracy than ordinary BP model and GWO-BP model.

关键词： consumer confidence index grey wolf optimization algorithm inertia weight location update neural network

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Fault Diagnosis Of Power Transformer Based On Extreme Learning Machine Optimized By Improved grey wolf optimization algorithm

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2023年第4期27卷 2367-2374页

作者： Xu, Yong Lu, Xiaojuan Zhu, Yuhang Wei, Jiawei Liu, Dan Bai, Jianchong Lanzhou Jiaotong Univ Sch Automat Elect Engn Lanzhou 730070 Peoples R China

关键词： Fault diagnosis Extreme learning machine Random forest grey wolf optimization algorithm Power transformer

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Multi-UAV Three-Dimensional Path Planning Based on Improved grey wolf optimization algorithm

Multi-UAV Three-Dimensional Path Planning Based on Improved ...

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第43届中国控制会议

作者： Mingyu Zhang Feng Liu Yujie Wang Yaqing Yan ZhengXian Wei Institute of Advanced Control Systems Beijing Jiaotong University Systems Engineering Research Institute China State Shipbuilding Corporation

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To address the challenges of multi-unmanned aerial vehicle(UAV) trajectory planning in three-dimensional complex environments, this study proposes a method based on the Improved grey wolf optimization algorithm for Multi-UAV 3D Trajectory Planning. The approach simulates real geographical environments, establishing three-dimensional terrain and no-fly zone models. Building upon the foundation of single UAV trajectory planning, the proposed method incorporates collaborative constraints for multi-UAV coordination, forming an evaluation function for multi-UAV collaborative trajectory planning. In order to solve the limitations of the standard grey wolf algorithm, which is prone to local optima and exhibits suboptimal convergence rates, an improved convergence factor strategy and a reward-penalty mechanism in the optimization process are introduced. Comparative evaluations against several relevant algorithms validate the superior feasibility of the proposed approach. Simulation results demonstrate that, compared to other algorithms, the proposed method achieves smaller trajectory costs, faster convergence rates, and more stable performance.

关键词： grey wolf optimization algorithm Multi-UAV 3D Trajectory Planning Convergence Factor Reward-Penalty Mechanism

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Research and application of an evolutionary deep learning model based on improved grey wolf optimization algorithm and DBN-ELM for AQI prediction

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SUSTAINABLE CITIES AND SOCIETY 2022年第0期87卷

作者： Li, Yiman Peng, Tian Hua, Lei Ji, Chunlei Ma, Huixin Nazir, Muhammad Shahzad Zhang, Chu Huaiyin Inst Technol Fac Automat Huaian Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian Peoples R China

Accurate forecast of air quality index (AQI) can provide reliable guarantee for air quality early warning and safe production. In this paper, a hybrid model for predicting AQI is presented. Firstly, the original AQI data is decomposed into multiple intrinsic mode functions (IMFs) components by using time varying filter based empirical mode decomposition (TVFEMD). To reduce the amount of calculation, sample entropy (SE) is intro-duced to estimate multiple IMF components. Secondly, the grey wolf optimization (GWO) algorithm was improved, the dimension learning-based hunting (DLH) search strategy was introduced to avoid falling into local optimum. Meanwhile, the opposite search strategy was introduced in the initialization of DLH strategy to enrich population information. Thirdly, the parameters of deep belief network -extreme learning machine (DBN-ELM) model is optimized by IGWO algorithm. Then the DBN-ELM model with optimal parameters are used to forecast each IMF component, respectively. Finally, the predicted value of each IMF component is reconstructed to get the total AQI predicted value. The comparison between the presented model and the other benchmark models used in this paper shows that presented model is better than other model in accuracy and generalization, which demonstrates that the presented model can effectively predict AQI.

关键词： Air quality index forecast Deep belief network Extreme learning machine grey wolf optimization algorithm Time varying filter based empirical mode decomposition

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Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Petrovic, Milica Jokic, Aleksandar Miljkovic, Zoran Kulesza, Zbigniew Univ Belgrade Fac Mech Engn Dept Prod Engn Lab Ind Robot & Artificial Intelligence Kraljice Marije 16 Belgrade 35 Serbia Bialystok Tech Univ Fac Elect Engn Dept Automat Control & Robot Wiejska 45D PL-15351 Bialystok Poland

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic -based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective grey wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi -objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the-art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outper-forms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on

关键词： Multi-objective optimization Scheduling of robotic systems Population-based metaheuristics grey wolf optimization algorithm Intelligent manufacturing systems

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