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

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

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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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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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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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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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The improvement of glowworm swarm optimization for continuous optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第7期39卷 6335-6342页

作者： Wu, Bin Qian, Cunhua Ni, Weihong Fan, Shuhai Nanjing Univ Technol Dept Ind Engn Nanjing 210009 Peoples R China

glowworm swarm optimization (GSO) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to enhances accuracy and convergence rate of the GSO, two strategies about the movement phase of GSO are proposed. One is the greedy acceptance criteria for the glowworms update their position one-dimension by one-dimension. The other is the new movement formulas which are inspired by artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). To compare and analyze the performance of our proposed improvement GSO, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the improvement algorithms are discussed by uniform design experiment. Numerical results reveal that the proposed algorithms can find better solutions when compared to classical GSO and other heuristic algorithms and are powerful search algorithms for various global optimization problems. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： glowworm swarm optimization algorithm Artificial bee colony algorithm Particle swarm optimization Uniform design Continuous optimization

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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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An Adaptive Radial Basis Function Neural Network glowworm swarm optimization for Time Series Forecasting 18

An Adaptive Radial Basis Function Neural Network Glowworm Sw...

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2nd International Conference on Intelligent Systems, Metaheuristics and swarm Intelligence (ISMSI)

作者： Isimeto, Roselyn Sennaike, Oladipupo A. Alienyi, Daniel C. Uwadia, Charles O. Univ Lagos Dept Comp Sci Lagos Nigeria Univ Lagos Dept Chem Engn Lagos Nigeria

ISBN: (纸本)9781450364126

The past three decades have witnessed active research using a class of Artificial Neural Network models (ANN), the Radial Basis Function Neural Networks, to forecast time series. Many techniques for forecasting time series using Radial Basis Function Neural Networks (RBFNN) have been proposed and developed in literature. The major challenges in RBFNN lie in the optimization of its full parameters: the number and location of cluster centres as well as the output weights. To address these challenges, this study adapted the Clustering Analysis based on glowworm swarm optimization (CGSO) algorithm to obtain a modified Clustering Analysis based on glowworm swarm optimization (CGSOm) algorithm for solving the clustering problem. Adaptation was achieved by incorporating a mechanism that determines the sensor range of the CGSO efficiently and automatically, and modifying the glowworm initialization method. For the weight optimization, the Bioluminescence swarm optimization algorithm (BSO) was adopted, making it the first time it will be applied in training the weights of the RBFNN. Other training algorithms tested include Conjugate Gradient Descent (CGD), Gradient Descent (GD) and Particle swarm optimization algorithm (PSO). Stock price and currency exchange rate data were used to train the combinations of models developed. The results obtained from the training showed that the CGSOm-CGD RBFNN gave best forecasting accuracy by yielding lowest error values;followed by the CGSOm-BSO RBFNN that gave relatively similar error values. Hence, two new training methodologies for time series forecasting resulted from this study;they are the CGSOm-BSO RBFNN and the CGSOm-CGD RBFNN.

关键词： Radial Basis Function Neural Network Time Series Forecasting swarm Intelligence Clustering algorithms glowworm swarm optimization algorithm

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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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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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