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Parameter tuning of auto disturbance rejection controller based on improved glowworm swarm optimization algorithm

ASSEMBLY AUTOMATION 2022年第4期42卷 427-444页

作者： Gao, Bingwei Shen, Wei Dai, Ye Ye, Yong Tai Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin Peoples R China

Purpose This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications. Design/methodology/approach This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters. Findings The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short;there is no overshoot;the tracking process is relatively stable;the anti-interference ability is strong;and the optimization effect is better. Originality/value The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.

关键词： Auto-disturbance rejection controller glowworm swarm optimization algorithm Parameter tuning Particle swarm optimization algorithm External disturbance

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Multi-objective optimization of experimental and analytical residual stresses in pre-stressed cutting of thin-walled ring using glowworm swarm optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第9-10期107卷 3897-3908页

作者： Zhang, Chengyan Wang, Lixin Zu, Xiaoli Meng, Wuzhou Anyang Inst Technol Sch Mech Engn Anyang 455000 Peoples R China

In this study, an analytical prediction model was utilized to predict the residual stresses induced during pre-stressed cutting of thin-walled ring, and the results were validated by experiments. General multivariate regression predictive models were presented to characterize the relationship between machining parameters and key characteristic values of residual stresses. These predictive models were used to build multi-objective optimization models to select the optimal machining parameters in process planning decision. The objectives are to minimize the tensile surface residual stress and to maximize the maximum compressive residual stress and processing efficiency and be chosen as related to industrial applications. An adaptive step-size multi-objective glowworm swarm optimization (MOGSO) algorithm was employed in optimizing parameters including cutting speed, cutting feed and pre-tightening torque. optimization results demonstrate the superiority of the improved algorithm over the traditional MOGSO. The optimum machining parameters calculated from the predicted results were represented in both objective function and decision variable spaces. Further experimental verification results verified the effectiveness of the optimization model.

关键词： Multi-objective optimization glowworm swarm optimization algorithm Residual stress Pre-stressed cutting

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Precise identification of moving vehicular parameters based on improved glowworm swarm optimization algorithm

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2017年第5期25卷 694-709页

作者： Li, H. L. Lu, Z. R. Liu, J. K. Huang, M. Sun Yat Sen Univ Dept Appl Mech & Engn Guangzhou Peoples R China Sun Yat Sen Univ Res Ctr Intelligent Transportat Syst Guangzhou Peoples R China

This paper proposes an indirect method for the identification of moving vehicular parameters using the dynamic responses of the vehicle. The moving vehicle is modelled as 2-DOF system with 5 parameters and 4-DOF system with 12 parameters, respectively. Finite element method is used to establish the equation of the coupled bridge-vehicle system. The dynamic responses of the system are calculated by Newmark direct integration method. The parameter identification problem is transformed into an optimization problem by minimizing errors between the calculated dynamic responses of the moving vehicle and those of the simulated measured responses. glowworm swarm optimization algorithm (GSO) is used to solve the objective function of the optimization problem. A local search method is introduced into the movement phase of GSO to enhance the accuracy and convergence rate of the algorithm. Several test cases are carried out to verify the efficiency of the proposed method and the results show that the vehicular parameters can be identified precisely with the present method and it is not sensitive to artificial measurement noise.

关键词： glowworm swarm optimization algorithm bridge-vehicle system dynamic response moving vehicle parameter identification

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The Variation Step Adaptive glowworm swarm optimization algorithm in Optimum Log Interpretation for Reservoir with Complicated Lithology 12

The Variation Step Adaptive Glowworm Swarm Optimization Algo...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Mo, Xiuwen Li, Xiao Zhang, Qiang Jilin Univ Coll Geoexplorat Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781509040933

Inversion of geophysical logging data is one of the most important tasks in oil and gas exploration. Ambiguity is usually inherent for the solutions, especially for formation with complex lithology. The optimum log interpretation technique can effectively reduce the ambiguity of the interpretation results. Therefore, the glowworm swarm optimization (GSO), one of the swarm intelligence optimization algorithms, is introduced into the log interpretation to obtain the optimal solution by virtue of its strong ability both in local and global optimization. Moreover, in order to solve the problem of slow convergence speed in the later iteration process, adaptive step is integrated into glowworm swarm optimization to form the Variation Step Adaptive glowworm swarm optimization (VSAGSO) algorithm, which improves the accuracy and efficiency of optimizing. VSAGSO algorithm is applied for test in the tuffaceous sandstone reservoir in a certain oilfield. Comprehensively considering all kinds of errors and constraints, it could directly working-out the optimized results of reservoir parameters such as tuff content, shale content, skeleton mineral content and porosity in well accordance with the core data.

关键词： optimum well logging interpretation glowworm swarm optimization algorithm swarm intelligence optimization algorithm self-adaption tuffaceous siltstone reservoir

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Optimal sensor placement under uncertainties using a nondirective movement glowworm swarm optimization algorithm

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SMART STRUCTURES AND SYSTEMS 2015年第2期16卷 243-262页

作者： Zhou, Guang-Dong Yi, Ting-Hua Zhang, Huan Li, Hong-Nan Hohai Univ Coll Civil & Transportat Engn Nanjing 210098 Jiangsu Peoples R China Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

关键词： structural health monitoring optimal sensor placement glowworm swarm optimization algorithm information entropy multi-objective optimization

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Hybridization of Pigeon inspired with glowworm' swarm optimization based clustering technique in wireless sensor networks

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MICROPROCESSORS AND MICROSYSTEMS 2022年 91卷 1页

作者： Alamelu, R. M. Prabu, K. Bharathidasan Univ Sudharsan Coll Arts & Sci PG & Res Dept Comp Sci Pudukkottai 622104 Tamil Nadu India

In wireless sensor networks (WSN), clustering is treated as an energy efficient technique employed to achieve maximum network lifetime. But, the process of cluster head (CH) selection for stabilized network operation and prolonged network lifetime remains a challenging issue in WSN. To resolve this issue, this paper presents a new hybridization of pigeon inspired with glowworm swarm optimization (HPIGSO) algorithm based clustering technique in WSN. The proposed HPIGSO algorithm integrates the good characteristics of pigeon inspired optimization (PIO) algorithm and glowworm swarm optimization (GSO) algorithm. The proposed algorithm operates on three major stages namely initialization, CH selection and cluster construction. Once the nodes are deployed, initialization process takes place. Followed by, base station (BS) executes the HPIGSO algorithm and selects the CHs effectively. Subsequently, nearby nodes joins the CH and becomes cluster members (CMs), thereby cluster construction takes place. Finally, the CMs send the data to CHs which is then forwarded to BS via inter-cluster communication. The proficient performance of the HPIGSO method has been evaluated and the results portrayed that the HPIGSO algorithm prolonged the lifetime of WSN over the existing clustering techniques.

关键词： Clustering Network lifetime Pigeon inspired optimization algorithm glowworm swarm optimization algorithm CH selection

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Harmonic Detection for Harmonic/Inter-Harmonic Based on Improved glowworm swarm optimization and Neural Network

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第12期15卷 1769-1779页

作者： Zhao, Yuewei Zhen, Zhigang Cui, Jingkai Wang, Yajing Shandong Univ Technol Shandong Coll Elect & Elect Engn Zibo Shandong Peoples R China

The conventional artificial neural network (ANN) model is an effective way to detect harmonic signals. However, the solution of the model cannot converge when the initial value of the excitation function (IVEF) is not suitable, causing a large error in harmonic detection, especially in the case of power system noise. We improve the back propagation (BP) neural network (BNN) model by using an adjustable excitation function and the basic inertial algorithm in this paper. Based on this model and combined with the glowworm swarm optimization (GSO) algorithm, a harmonic/inter-harmonic detection method is proposed. The network first uses an improved GSO algorithm with an adaptive step to optimize the IVEF of BNN. Then, BNN is trained at this initial value, so that amplitude, phase, and frequency of harmonics/inter-harmonics can be obtained. The simulation data analyses show that that the method has good stability, convergence, strong anti-noise ability, and ultrahigh detection accuracy. It can accurately separate the integer and non-integer harmonics. Compared with the traditional ANN algorithm and Hanning-FFT, the detection accuracy of this algorithm can be improved by 2-4 orders of magnitude. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： glowworm swarm optimization algorithm neural network model adjustable excitation function harmonic detection inter-harmonic

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Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷

作者： Gomathi, P. Muniraj, C. Periasamy, P. S. VSB Engn Coll Dept Elect & Commun Engn Karur 639111 Tamil Nadu India Knowledge Inst Technol Dept Elect & Elect Engn Salem 637504 Tamil Nadu India KSR Coll Engn Dept Elect & Commun Engn Tiruchengode 637215 Tamil Nadu India

Medical Research field has been taken continuous efforts to develop an efficient method for detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D U-Net segmentation using digital infrared (IR) thermal imaging system is proposed in this manuscript for the diagnosis of breast cancer (DBC-4D U-Net-DITI). Initially, the digital infrared thermal images are taken from DMR-IR data set as input, and the imageries are pre-processed to maintain local features and compress the dynamic range of image based upon Altered Phase Preserving Dynamic Range Compression (APPDRC) approach by removing the speckle noise. Then, the image segmentation is carried out with the help of 4D U-Net for obtaining the segmented digital infrared thermal image. The 4D U-Net weight parameters are optimized with glowworm swarm optimization algorithm (GSOA). The segmented regions of digital infrared thermal images are fed to Binarized Spiking Neural Network (BSNN) for classifying the pathology stage as No spread, Early Stage, Localized, Regional and Distant. The proposed approach is executed in MATLAB. The performance of proposed approach attains better accuracy of 39.01%, 28.34%, and 37.45%, better precision of 17.12%, 24.12% and 32.07% when compared to existing approaches like chaotic salp swarm algorithm (CSSA) based segmentation of thermal images for breast cancer identification (DBC-CSSA-DITI), marine-predators-algorithm based segmentation of thermal images for the diagnosis of breast cancer (DBC-MPA-DITI) and diagnosis of breast cancer based upon CNN using thermal im-ageries (DBC-CNN-DITI) respectively.

关键词： Breast cancer Segmentation Digital infrared thermal image Mathematical modeling Altered Phase Preserving Dynamic Range Compression 4D U-Net glowworm swarm optimization algorithm Binarized Spiking Neural Network

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Load Forecasting model of Mobile Cloud Computing Based on glowworm swarm optimization LSTM Network 2019

Load Forecasting model of Mobile Cloud Computing Based on Gl...

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Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City

作者： Zhenhua Zhang Wei Zhu Wei Zhong Yi Zhuang Jiangsu Automation Research Institute Lianyungang China Nanjing University of Aeronautics and Astronautics Department of computer science and technology Nanjing China

ISBN: (纸本)9781450376631

Aiming at the problem of host load forecasting in mobile cloud computing, the Long Short Term Memory networks (LSTM) is introduced, which is suitable for the complex and long-time series data of the cloud environment and a load forecasting algorithm based on glowworm swarm optimization LSTM neural network is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the glowworm swarm optimization algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are superior to similar prediction algorithms in prediction accuracy.

关键词： glowworm swarm optimization algorithm Load Forecasting LSTM Network Mobile cloud

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A multi-objective service composition recommendation method for individualized customer: Hybrid MPA-GSO-DNN model

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 122-134页

作者： Liu, Zhengchao Guo, Shunsheng Wang, Lei Du, Baigang Pang, Shibao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China

With the development of manufacturing customization, unified manufacturing service recommendation is difficult to meet the customer's individualized demand. To this end, the existing research hotspots focus on solving personalized service recommendation issues. However, the personalized recommendation for service composition is more complex compared with the existing single service recommendation. Especially in the case of less customer historical data, analyzing customer preference and recommending appropriate composite service is a difficult problem. Therefore, this paper proposes a hybrid MPA-GSO-DNN model based on manufacturing service to address the personalized recommendation problem for service composition. Firstly, a hybrid multi-objective preference analysis model and glowworm swarm optimization algorithm (MPA-GSO) is proposed to generate deep learning training set by analyzing customer preference and repetitively simulating the customer's selection process. The glowworm swarm optimization (GSO) algorithm is improved with dynamic step to solve the continuous multi-objective optimization in MPA-GSO. Secondly, a deep neural network (DNN) is structured to analyze candidate services and provide personalized recommendation. Finally, a case study is presented to demonstrate the performance and practicability of the proposed approach.

关键词： Personalized recommendation Multi-objective preference analysis Service composition Deep learning glowworm swarm optimization algorithm

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