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Point Cloud Registration algorithm Based on the grey wolf Optimizer

IEEE ACCESS 2020年 8卷 143375-143382页

作者： Feng, Yaqing Tang, Jialin Su, Binghua Su, Qinglang Zhou, Zhuang Beijing Inst Technol Opt Engn Zhuhai 519088 Peoples R China Beijing Inst Technol Minist Educ China Key Lab Photoelect Imaging Technol & Syst Zhuhai 519088 Peoples R China Beijing Inst Technol Fac Informat Technol Zhuhai 519088 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China City Univ Macau Res Inst Data Sci Macau Peoples R China

In view of the long computation time and low registration accuracy of the current point cloud registration algorithm, a point cloud registration algorithm based on the grey wolf optimizer (GWO) is proposed, denoted PCR-GW. The algorithm uses the centralization method to solve the translation matrix and then simplifies the points of the initial point cloud models by using the intrinsic shape signatures (ISS) feature. Next, various parameters of the rotation matrix are obtained via the GWO algorithm by employing the quadratic sum of the distances between corresponding points in the simplified point cloud as the objective function. Finally, the point cloud registration process is completed by using the obtained transformation matrix. By conducting a registration experiment on the point cloud library model and comparing PCR-GW with the traditional algorithms, the algorithm proposed in this article is shown to be promising for improving the computation speed and registration accuracy.

关键词： Point cloud registration feature point extraction grey wolf optimization algorithm

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An Optimized Self-Compensated Solution for Temperature and Strain Cross-Sensitivity in FBG Interrogators Based on Edge Filter

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SENSORS 2021年第17期21卷 5828-5828页

作者： Silveira, Mariana L. Rocha, Helder R. O. Antunes, Paulo F. C. Andre, Paulo S. B. Segatto, Marcelo E., V Frizera, Anselmo Diaz, Camilo A. R. Univ Fed Espirito Santo Elect Engn Dept Telecommun Lab LABTEL BR-29075910 Vitoria ES Brazil Univ Aveiro I3N P-3810193 Aveiro Portugal Univ Aveiro Phys Dept P-3810193 Aveiro Portugal Inst Telecomunicacoes Campus Univ Santiago P-3810193 Aveiro Portugal Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Dept Elect & Comp Engn P-1049001 Lisbon Portugal

Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry-Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 degrees C led to an average relative error of 3.4% for strain measurements up to 700 mu epsilon. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.

关键词： fiber optical sensors fiber Bragg gratings edge filter temperature cross-sensitivity grey wolf optimization algorithm

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Multi-Objective Optimal Control Method for the 6-DOF Robotic Crusher

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9397页

作者： Duan, Guochen Yao, Lele Zhan, Zhanyu Kang, Tao Guo, Chaoyue China North Vehicle Res Inst Beijing 100072 Peoples R China

In order to achieve the best crushing effect of the 6-DOF robotic crusher, a multi-objective optimal control method for the 6-DOF robotic crusher has been proposed. Taking the mass fraction of crushed products below 12 mm, total energy consumption, effective energy consumption, output, and wear as the working indexes, and taking the suspension point, precession angle, and swing frequency of the mantle as the working conditions of the crusher, the working indexes under different working conditions are calculated. And, based on the above parameters, the optimization objective function of the 6-DOF robotic crusher is obtained. The weight determination method of fuzzy multiple attributes decision making (FMADM) is used to determine the equivalent wear and the optimization target weight. Compared with the original scheme, the output increases and the energy consumption decreases significantly. The results can be used as a reference for the control strategy of the 6-DOF robotic crusher. It can also be used as a reference for the design of a traditional cone crusher.

关键词： 6-DOF robotic crusher multi-objective optimization fuzzy multiple attributes decision making (FMADM) grey wolf optimization algorithm

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Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

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ENERGY 2024年 304卷

作者： Sun, Xiaodong Chen, Zongzhe Han, Shouyi Tian, Xiang Jin, Zhijia Cao, Yunfei Xue, Mingzhou Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

关键词： Energy management strategy Equivalent consumption minimization strategy (ECMS) Plug-in hybrid electric bus (PHEB) grey wolf optimization algorithm

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Research on Two-Stage Semi-Active ISD Suspension Based on Improved Fuzzy Neural Network PID Control

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SENSORS 2023年第20期23卷 8388页

作者： Jin, Linhao Fan, Jingjing Du, Fu Zhan, Ming North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China Beijing Inst Technol Sch Mech Vehicular Engn Beijing 100081 Peoples R China

To better improve the ride comfort and handling stability of vehicles, a new two-stage ISD semi-active suspension structure is designed, which consists of the three elements, including an adjustable damper, spring, and inerter. Meanwhile, a new semi-active ISD suspension control strategy is proposed based on this structure. Firstly, the fuzzy neural network's initial parameters are optimized using the grey wolf optimization algorithm. Then, the fuzzy neural network with the optimal parameters is adjusted to the PID parameters. Finally, a 1/4 2-degree-of-freedom ISD semi-active suspension model is constructed in Matlab/Simulink, and the dynamics simulation is carried out for the three schemes using PID control, fuzzy neural network PID control, and improved fuzzy neural network PID control, respectively. The results show that compared with adopting PID control and fuzzy neural network PID control strategy, the vehicle body acceleration and tire dynamic loads are significantly reduced after using the grey wolf optimized fuzzy neural network PID control strategy, which shows that the control strategy proposed in this paper can significantly improve the vehicle smoothness and the stability of the handling.

关键词： two-stage ISD semi-active suspension grey wolf optimization algorithm fuzzy neural network Matlab/Simulink

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A Guided Mutation grey wolf Optimizer algorithm Integrating Flower Pollination Mechanism

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电脑学刊 2022年第2期33卷 51-67页

作者： Jing-Sen Liu Qing-Qing Liu Fang Zuo

The basic grey wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm's overall optimization performance is improved. The teaching mechanism is added to α wolf to improve the algorithm's convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm's accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.

关键词： grey wolf optimization algorithm flower pollination algorithm teaching mechanism polynomial mutation CEC2017

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An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

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Procedia Computer Science 2022年 215卷 556-566页

作者： Suman Sansanwal Nitin Jain Research Scholar Chandigarh University Mohali 140413 India Professor Chandigarh University Mohali 140413 India

Cloud computing has shown noteworthy evolution in information technology. Users can enjoy various services of cloud technology only if internet connection is available. In cloud computing, load balancing considered as a fundamental issue that has confronted researchers in this domain. Load balancing basically works by allotting fair and efficient work among computing resources which ultimately achieve high user satisfaction and raises systems productivity. Many load-balancing techniques made efforts to resolve this problem using metaheuristics algorithm, and amplify the operation and efficiency of systems. In this paper, existing load balancing techniques has been discussed and research gaps in existing lirture hasbeeb discussed. Also a new technique has been proposed called IG-GWO - Inquisitive Genetic grey wolf optimization algorithm using combination of grey wolf optimization (GWO) algorithm and Genetic algorithm.

关键词： Cloud computing load balancing grey wolf optimization algorithm Cloudsim

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Intelligent Temperature Control Method of Instrument Based on Fuzzy PID Control Technology

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 927-936页

作者： Li, Wenfang Wang, Yuqiao Huanghe Sci & Technol Univ Fac Engn Zhengzhou 450063 Peoples R China

The current instrumentation intelligent temperature control is generally realized based on PID control technology, whose efficiency and precision are low and cannot meet the actual production requirements. A fuzzy PID (FPID) control technique is suggested as a solution to this issue with the goal to increase the control precision by adjusting the PID parameters in real-time using a fuzzy algorithm. In addition, a multi -strategy -fused Improved grey wolf optimization (MGWO) algorithm is used to obtain the optimal fuzzy rule parameters for the fuzzy controller to achieve the optimization of FPID. In addition to the aforementioned, the MGWO-FPID-based instrumentation intelligent temperature control model is created to enhance the instrumentation's ability to regulate temperature. The testing results demonstrated that the MGWO-FPID model outperformed the other two models with values for the objective function of 5 10-8, adaptation degree of 13.1, control regulation time of 2.08 s, F1 value of 96.14%, MAE value of 8.53, Recall value of 95.37%, and AUC value of 0.995. The above results prove that the MGWO-FPID-based instrumentation intelligent temperature control model proposed in the study has high accuracy and efficiency, which can effectively realize the instrumentation intelligent temperature control in industrial production, and then improve the accuracy and efficiency of instrumentation temperature control, ensure the safe production of industry, and promote the industrial development to a certain extent. This model can monitor and regulate the temperature in the industrial production process in real time, avoiding safety accidents caused by temperature anomalies, and ensuring the safety of industrial production. And the application of this model can improve the efficiency and product quality of industrial production, help reduce production costs and improve economic benefits. This can not only promote the development of related industries, but also drive the econo

关键词： Fuzzy PID control instrumentation intelligent temperature control differential negative feedback grey wolf optimization algorithm

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Energy-saving optimal scheduling under multi-mode "source-network-load-storage" combined system in metro station based on modified Gray wolf algorithm

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ARCHIVES OF ELECTRICAL ENGINEERING 2024年第1期73卷 121-143页

作者： Tian, Jingjing Qian, Yu Zhao, Feng Mo, Shenglin Xiao, Huaxuan Zhu, Xiaotong Liu, Guangdi Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou Peoples R China Minist Educ Key Lab Optotechnol & Intelligent Control Lanzhou Peoples R China

Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi -modal "source-network-load-storage" system in metro stations. The proposed method, called the Improved Gray wolf optimization algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the "sourcenetwork-load-storage" joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray wolf optimization algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.

关键词： bi-level optimization grey wolf optimization algorithm multi-mode peak shaving and valley filling source-network-load-storage

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CONSTRUCTION AND APPLICATION OF A BEARING FAULT DIAGNOSIS MODEL BASED ON IMPROVED GWO algorithm

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Diagnostyka 2024年第3期25卷

作者： Jiang, Lingbo School of Mechanical and Electronic Engineering Shandong Vocational College of Industry Zibo256414 China

In mechanical equipment, if bearing components fail, it can cause serious equipment damage and even threaten human life safety. Therefore, equipment bearings fault diagnosis is of great meaning. In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm, and then optimizes the penalty factor and KF parameters of the support vector machine to enhance the accuracy of fault classification. At the same time, in the problem of fault identification, the introduction of adaptive noise set empirical mode decomposition and the combination of permutation entropy are studied to minimize the impact of noise on the identification model. The experimental outcomes indicated that the algorithm proposed in the study had an average fitness value and a standard deviation fitness value of 0 in the benchmark test function and 94.55% accuracy in overall fault identification. The permutation entropy of the vibration signal in the normal state of the bearing was within the range of [0.13, 0.52], which has a more stable state compared to the fault state. The results show that the improved grey wolf optimization algorithm is applied to the optimization of support vector machine, combined with the adaptive noise set empirical mode decomposition and permutation entropy, and the identification and classification results of bearing faults are successfully improved, making the proposed method feasible in bearing fault diagnosis, and providing a more effective scheme for fault diagnosis. © 2024 by the Authors. Licensee Polish Society of Technical Diagnostics (Warsaw. Poland).

关键词： bearings fault grey wolf optimization algorithm permutation entropy support vector machine

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