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Estimating soot volume fraction and temperature in flames using stochastic particle swarm optimization algorithm

INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2011年第1-3期54卷 217-224页

作者： Sun, Yi-Peng Lou, Chun Zhou, Huai-Chun Huazhong Univ Sci & Technol State Key Lab Coal Combust Wuhan 430074 Hubei Peoples R China

A simulation investigation for simultaneous reconstruction of distributions of temperature and soot volume fraction from multi-wavelength emission in a sooting flame using the stochastic particle swarm optimizer (PSO) algorithm is presented. The self-absorption of the flame is considered. The selection of parameters of the stochastic PSO algorithm and detection wavelengths is analyzed. The effects of measurement errors and optical thickness of the flame on the accuracy of the reconstruction are investigated. It proved that the stochastic PSO algorithm is robust and can obtain accurate distributions of temperature and soot volume fraction from line-of-sight intensities in only several wavelengths, especially in the flame with large optical thickness, while other methods neglecting self-attenuation of the flame will take some errors. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Inverse radiation problem particle swarm optimization algorithm Flame emission spectrum Soot volume fraction Temperature reconstruction

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Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm

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ATMOSPHERIC POLLUTION RESEARCH 2019年第5期10卷 1482-1491页

作者： Liu, Wei Guo, Geng Chen, Fuji Chen, Yihui Fuzhou Univ Sch Econ & Management Fuzhou 350116 Fujian Peoples R China

Daily PM2.5 level has significant influence on human health, which is attracting increasing attention. The prediction of PM2.5 grades has thus become an important factor closely related to social development. In the past decades, many prediction methodologies for PM2.5 have been developed, including regression analysis, neural network model, and support vector machine model. Despite these progresses, it still remains a great challenge to predict the PM2.5 grades more accurately and efficiently. In this work, we applied meteorological pattern analysis to assist the support vector machine (SVM) model for PM2.5 class prediction. Cosine similarity was first used to extract three most relevant ones from six common meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed, wind direction, cumulative precipitation) to give the needed meteorological pattern for SVM model. Higher prediction accuracy was then obtained with the selected pattern composed by relative humidity, wind speed and wind direction. Moreover, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm were investigated for optimizing the parameters in the process of SVM classification, with PSO-SVM presenting the highest accuracy and efficiency (forecasting time significantly reduced by 25%). We further introduced the criteria of precision, recall and F1-score to evaluate the prediction results of PSO-SVM in each PM2.5 grade. Meanwhile, comparative studies confirmed that PSO-SVM displayed better performance than Adaboost and ANN models for the applied meteorological pattern analysis assisted PM2.5 grades prediction. These obtained results indicate the validity of meteorological pattern analysis for efficient air quality forecasting.

关键词： PM2.5 grades Meteorological pattern analysis SVM particle swarm optimization algorithm Air pollution

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An Improved particle swarm optimization algorithm with Harmony Strategy for the Location of Critical Slip Surface of Slopes

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China Ocean Engineering 2011年第2期25卷 357-364页

作者：李亮褚雪松 School of Civil Engineering Qingdao Technological University

The determination of optimal values for three parameters required in the original particle swarm optimization algorithm is very difficult. It is proposed that two new parameters simulating the harmony search strategy can be adopted instead of the three parameters which are required in the original particle swarm optimization algorithm to update the positions of all the particles. The improved particle swarm optimization is used in the location of the critical slip surface of soil slope, and it is found that the improved particle swarm optimization algorithm is insensitive to the two parameters while the original particle swarm optimization algorithm can be sensitive to its three parameters.

关键词： slope stability analysis limit equilibrium method particle swarm optimization algorithm harmony strategy

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A parameter determination method of unscented transformation and its approximate ability analysis in the precision estimation of nonlinear measurement adjustment

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MEASUREMENT 2020年 166卷 108065-108065页

作者： Wang, Leyang Ding, Rui East China Univ Technol Fac Geomat Nanchang 330013 Jiangxi Peoples R China Shandong Jianzhu Univ Sch Surveying & Geoinformat Jinan 250101 Peoples R China

Unscented transformation (UT) is the key technology of unscented Kalman filter, which can approximate probability distribution to estimate second-order approximate mean and covariance matrix of nonlinear function. In parameter selection of various sampling strategies in the framework of the unscented transformation (UT), there are few studies on the determination method of sampling parameters. In this study, the particle swarm optimization (PSO) algorithm is introduced into UT for high convergence efficiency and few control parameters. The parameter selection of several commonly used sampling strategies is analyzed from the numerical point of view, and a method to determine the sampling parameters is proposed. Furthermore, the higher-order information ignored by the recommended parameters of UT is considered, and the approximation ability of UT is tapped and improved by the obtained parameters. For the first time, the minimal skew simplex sampling (MSS) and the scaled minimal skew simplex sampling (SMSS) are applied to the precision estimation of nonlinear measurement adjustment. Considering the influence of non-normal distribution data on nonlinear error propagation in geodetic measurement, this study analyzes the PSO-UT model in a case study from the geodetic field. Experiments show that PSO can effectively determine the parameters of UT and improve its approximation ability, which enriches UT theory. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Nonlinear measurement adjustment Precision estimation Unscented transformation particle swarm optimization algorithm Parameter selection Non-normal distribution

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Evolutionary dynamics in spatial threshold public goods game with the asymmetric return rate mechanism

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CHAOS SOLITONS & FRACTALS 2020年 136卷 109819-109819页

作者： Wang, Xianjia Chen, Wenman Wuhan Univ Econ & Management Sch Wuhan 430070 Peoples R China Wuhan Univ Inst Syst & Engineer Wuhan 430070 Peoples R China

In reality, cooperators often are provided a higher return rate for their contributions. Inspired by the reality, this paper introduces the asymmetric return rate mechanism, where the return rate is asymmetric between cooperators and defectors. This paper mainly studies how the asymmetric return rate mechanism influences the evolutionary dynamics in spatial threshold public goods game on two different complex networks, the namely square lattice and Barabási-Albert scale-free network. The simulation results show that increasing the sensitivity for the spread of cooperation is more effective than increasing that for the spread of defection not only to promote cooperation, but also to elevate the provision of the public goods. In addition, a moderate value of threshold is the best to elevate both the promotion of cooperation and the provision of the public goods.

关键词： Evolutionary dynamics Spatial threshold public goods game Asymmetric return rate particle swarm optimization algorithm

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Non-uniformly spaced linear antenna array design by means of PEEC approach applying Cheetah optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2019年第Sup1期60卷 S15-S24页

作者： Moreno, Sinvaldo R. Klein, Carlos E. Mariani, Viviana C. Coelho, Leandro dos S. Alotto, Piergiorgio Torchio, Riccardo Bauernfeind, Thomas Baumgartner, Paul Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind Syst Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Padua Dipartimento Ingn Ind Padua Italy Graz Univ Technol Inst Fundamentals & Theory Elect Engn Graz Austria

This paper presents the application of the Partial Element Equivalent Circuit (PEEC) approach, which is a full wave electromagnetic modelling technique for conductors embedded in arbitrary dielectrics based on equivalent circuits, to the optimal design of antennas with non-uniform spacing between the array elements. The design optimization problem is solved by means of the new nature-inspired Cheetah metaheuristic. The main aim of this paper is to introduce the Cheetah optimization algorithm to the electromagnetics and antenna community. The results are compared to two well-known optimization algorithms and to show the effectiveness of the proposed algorithm on a realistic benchmark problem.

关键词： Antenna array Cheetah optimization algorithm genetic algorithm partial element equivalent circuit particle swarm optimization algorithm

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Simulation optimization using particle swarm optimization algorithm with application to assembly line design

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APPLIED SOFT COMPUTING 2011年第1期11卷 605-613页

作者： Kuo, R. J. Yang, C. Y. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

Assembly line design is an important part of production system. Some processes need to undergo changes in order to increase in efficiency. Computer simulation has been applied on process design for many decades. Traditionally, simulation had to run all possible alternatives of assembly line and was not considered as an optimization technique. Thus, this study employs particle swarm optimization (PSO) algorithm which is with mutation based on similarity for simulation optimization in order to optimize the managerial parameters in production system. Through experimentation designs and statistics tests, the simulation results show that the proposed method is better than other algorithms, like genetic algorithm and conventional PSO algorithm for solving assembly line design problem. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Simulation particle swarm optimization algorithm Simulation optimization Assembly line

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An application of particle swarm optimization algorithm to clustering analysis

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SOFT COMPUTING 2011年第3期15卷 533-542页

作者： Kuo, R. J. Wang, M. J. Huang, T. W. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising.

关键词： Clustering analysis particle swarm optimization algorithm K-means

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Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model

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AGRICULTURAL WATER MANAGEMENT 2020年 227卷 105846-000页

作者： Jin, Xiuliang Li, Zhenhai Feng, Haikuan Ren, Zhibin Li, Shaokun Chinese Acad Agr Sci Inst Crop Sci Minist Agr Key Lab Crop Physiol & Ecol Beijing 100081 Peoples R China Beijing Acad Agr & Forestry Sci Beijing Res Ctr Informat Technol Agr Beijing 100097 Peoples R China Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China

The accurate and timely estimation of temporal and spatial changes in crop growth and yield before harvesting is essential for ensuring global food security. The integration of remote sensing data and crop models is a potential approach for the estimation of key crop growth parameters and crop yields. Therefore, the aim of this study was to assimilate biomass and canopy cover (CC) derived from vegetation indices into the AquaCrop model using the particle swarm optimization (PSO) algorithm in order to obtain a more accurate estimation of CC, biomass, and yield for maize. The results show that, compared to other vegetation indices, the enhanced vegetation index (EVI) and the three-band water index (TBWI) can be used to obtain a better estimation of CC (R-2 = 0.78 and root-mean-square error (RMSE) =9.84%) and biomass (R-2 = 0.76 and RMSE = 2.84 ton/ha), respectively. Additionally, it was found that the data assimilation approaches in which only CC was used as a state variable (scheme SVcc) and only biomass was used as a state variable (scheme SVbio) can be used to obtain more accurate estimations of CC (R-2 = 0.83 and RMSE = 8.12%) and biomass (R-2 = 0.81 and RMSE = 2.51 ton/ha), respectively;however, larger differences were found between the measured and estimated values of one variable (i.e., CC or biomass) when the other variable (i.e., biomass or CC) was used as the only state variable during the data assimilation. The data assimilation approach in which both CC and biomass were used as state variables (scheme SVcc+bio) produced a robust result, with the estimation accuracy being fairly close to that obtained using the single-variable (SVcc or SVbio) data assimilation approaches. The estimation accuracy for maize yield was slightly better when using a double-variable data assimilation approach (R-2 = 0.78 and RMSE = 1.44 ton/ha) than when using a single-variable data assimilation approach. In summary, this study presents a robust approach for increasing the estimat

关键词： Maize yield Canopy cover Biomass particle swarm optimization algorithm Double-state variables Data assimilation

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A Multi-Producer Group-Search-optimization Method-Based Maximum-Power-Point-Tracking for Uniform and Partial Shading Condition

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IEEE ACCESS 2020年 8卷 184688-184696页

作者： Li, Lijuan Chen, Yongdong Liu, Hongliang Tang, Wenbing Liu, Mengni Wu, Jun Li, Zeyu Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Xiangtan Univ Coll Math & Computat Sci Xiangtan 411105 Peoples R China Changde Power Supply Co State Grid Hunan Elect Power Co Changde 415001 Peoples R China

The power-voltage (P-U) curve of PV array shows multiple power points, which brings challenges to fast and accurately tracking of the global maximum power point. Considering the nonlinearity and the multi-peak characteristics of PV array output curve under the condition of partial shadow, a multi-producer group search optimization (MGSO) method for maximum power point tracking (MPPT) is proposed in this paper. In the MGSO, the characteristics and operations of three categories of members, including producers, scroungers, and rangers, are set according to the P-U characteristics. The number of producers is determined by the number of peaks. The initial position of each producer locates dispersedly to each peak region which makes the producers not fall into local optimum. The search strategy is simplified by the proposed angle transformation function of scroungers and omitting the ranger. The results of simulation and comparison demonstrate that the proposed MGSO method can effectively track the maximum power point under the uniform irradiance or partially shaded conditions, and also increase the utilization of solar energy.

关键词： Maximum power point trackers optimization Lighting Software algorithms particle swarm optimization Photovoltaic systems Licenses PV utilization group search optimization algorithm maximum power point tracking particle swarm optimization algorithm photovoltaic system

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