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Parameter identification of an SOFC model with an efficient, adaptive differential evolution algorithm

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2014年第10期39卷 5083-5096页

作者： Gong, Wenyin Cai, Zhihua Yang, Jie Li, Xi Jian, Li China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Sch Mech & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China

An efficient, adaptive differential evolution (DE) algorithm is proposed in which DE parameter adaptation is implemented. A ranking-based vector selection and crossover rate repairing technique are also presented. The method is referred to as DADE (Improved Jingqiao Adaptive DE). To verify the performance of DADE, the parameters of a simple SOFC electrochemical model that is used to control the output performance of an SOFC stack are identified and optimized. The SOFC electrochemical model is built to provide the simulated data. The results indicate that the proposed method is able to efficiently identify and optimize model parameters while showing good agreement with both simulated and experimental data. Additionally, when compared to other DE variants and other evolutionary algorithms, DADE obtained better results in terms of the quality of the final solutions, robustness, and convergence speed. Copyright (c) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithms Electrochemical model Parameter identification Solid oxide fuel cell (SOFC)

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An embedded differential evolution approach to adaptive covariance inflation with the ensemble Kalman filter

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第22期45卷 8425-8440页

作者： Bai, Yulong Yu, Qinghe Ma, Yongjie Northwest Normal Univ Coll Phys & Elect Engn 967 Anning East Rd Lanzhou Peoples R China

The limited ensemble sizes and the neglected model errors in ensemble-based Data Assimilation (DA) usually lead to underestimation of the error variance, and covariance inflation 'inflates' the covariance of a forecast or analysis ensemble by some positive factor in each assimilation cycle. In practice, the value of the inflation factor is often set by trial and error. Thus, a variety of adaptive covariance inflation (ACI) algorithms have been proposed to improve assimilation performance by adjusting the optimal parameters. Most of these used traditional optimization algorithms, such as gradient methods and relaxation methods. However, evolutionary algorithms(EAs), which encompass a range of population-based stochastic search techniques widely used in various engineering optimization problems, are rarely seen in DA. In this study, we developed a new framework for adaptive inflation factors, introducing differential evolution (DE) algorithm into local ensemble Transform Kalman filter (LETKF) that can explore the most suitable/optimal inflation factors online. A 'micro-DE' based data assimilation method can obtain better assimilation results step by step without taking a much longer time. The feasibility and effectiveness of the algorithm is demonstrated in an idealized Lorenz-96 model with simulated observations. Under both perfect and imperfect-model scenarios, it is found that the 'micro-DE'LETKF method is capable of outperforming the original LETKF with a considerable lower computational cost. The adaptive inflation factor is optimally updated at each assimilation cycle either around the upper bound or around the lower bound, which means that only an adaptive factor helps mitigate the ensemble collapse due to a lack of spread. However, the further study will consider the application of the methodology to more complex atmospheric models.

关键词： Ensemble Transform Kalman filter adaptive inflation differential evolution algorithms

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Further improvement of optimisation method for mass transit signalling block-layout design using differential evolution

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IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS 1999年第5期146卷 559-569页

作者： Chang, CS Du, D Natl Univ Singapore Dept Elect Engn Singapore 119260 Singapore

The paper describes the ongoing development of optimisation methods for layout design of equi-block n-aspcct mass transit signalling systems. The authors previously applied genetic algorithms (GAs) in place of conventional gradient search methods for solving the problem. Being theoretically and empirically sound for providing multiple-point search, the GA-based approach simplifies the gradient search approach, broadens the scope for dealing with changes of either the objective function or signalling scheme, and provides robust and global convergence in complex search spaces. The GA-based formulation divides an inter-station run into three sections: a constraint section, a stretchable section and a critical section. Since each of these sections was prescribed with a different design criterion, the OA optimises separately the layout of block joints in each section and their positions within each section. The solution lime for optimal signalling design is further reduced with the use of differential evolution (DE) algorithms. The DE-based method combines the three subordinate objective functions in the original GA algorithm into one composite objective function for one single search. The performance improvements of the DE algorithms over the GA-based method are shown. The choice of variants for solving the DE-based signalling design problem is also discussed.

关键词： multiple-point search rail traffic signalling scheme complex search spaces signalling Optimisation techniques subordinate objective functions gradient search approach genetic algorithms rail traffic control design performance improvements control system analysis objective function Rail-traffic system control optimal control rapid transit systems differential evolution algorithms Optimal control traffic control control system synthesis composite objective function mass transit signalling block-layout design Control system analysis and synthesis methods Control applications in remote signalling, dispatching and safety devices optimisation methods solution time design criterion

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Structures of three substituted arenesulfonamides from X-ray powder diffraction data using the differential evolution technique

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ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE 2002年第5期58卷 823-834页

作者： Tremayne, M Seaton, CC Glidewell, C Univ Birmingham Sch Chem Sci Birmingham B15 2TT W Midlands England Univ St Andrews Dept Chem St Andrews KY16 9ST Fife Scotland

The structures of three substituted arenesulfonamides have been solved from laboratory X-ray powder diffraction data, using a new direct-space structure solution method based on a differential evolution algorithm, and refined by the Rietveld method. In 2-toluenesulfonamide, C7H9NO2S (I) (tetragonal I4(1)/a, Z = 16), the molecules are linked by N-H...O=S hydrogen bonds into a three-dimensional framework. In 3-nitrobenzenesulfonamide, C6H6N2O4S (II) (monoclinic P2(1), Z = 2), N-H...O=S hydrogen bonds produce molecular ladders, which are linked into sheets by C-H...O=S hydrogen bonds: the nitro group does not participate in the hydrogen bonding. Molecules of 4-nitrobenzenesulfonamide, C6H6N2O4S (III) (monoclinic P2(1)/n, Z = 4), are linked into sheets by four types of hydrogen bond, N-H...O=S, N-H...O(nitro), C-H...O=S and C-H...O(nitro), and the sheets are weakly linked by aromatic pi...pi stacking interactions.

关键词： Crystal-structure determination C-methylated nitroanilines Genetic algorithm Patterns Solids X ray powder diffraction hydrogen bonds Rietveld method Substituted Genetic Algorithm (GA) differential evolution algorithms

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A CREATIVE differential evolution ALGORITHM FOR GLOBAL OPTIMIZATION PROBLEMS

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2013年第5期21卷 551-561页

作者： Kuo, Hsin-Chuan Lin, Ching-Hai Wu, Jeun-Len Natl Taiwan Ocean Univ Dept Syst Engn & Naval Architecture Keelung Taiwan

Under the framework of the differential evolution algorithm, we develop a creative algorithm by extrapolating the thinking of the garbage can model. The developed algorithm is hence named in the study as a creative differential evolution algorithm (CDE). To verify the performance of the CDE, we selected seven well-known benchmark functions;three of them are uni-modal and four multi-modal. In conducting the numerical experiment, we adopted two different numbers of dimensions for each test function, which is 50 and 100. The results show that CDE can find the global optimum robustly, demonstrate that CDE significantly improves the DE's performance.

关键词： global optimizations differential evolution algorithms garbage can decision-making model creative differential evolution algorithm

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Optimization of reinforcement layout of scissor-type bridge using differential evolution algorithm

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COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2019年第6期34卷 523-538页

作者： Chikahiro, Yuki Ario, Ichiro Pawlowski, Piotr Graczykowski, Cezary Holnicki-Szulc, Jan Shinshu Univ Dept Water Environm & Civil Engn Matsumoto Nagano Japan Hiroshima Univ Dept Civil & Environm Engn Hiroshima Japan Polish Acad Sci Inst Fundamental Technol Res Warsaw Poland

Scissors mechanisms are commonly used in safety engineering during the construction of temporary structures, owing to their inherent advantages of foldability, transformability, and reusability. We effectively utilized these scissors mechanism features to develop a lightweight, deployable emergency Mobile Bridge (MB) based on optimization, and control of the folding structure. Here, we discuss the problems of optimal reinforcement layout for the MB by formulating and solving three optimization problems, namely: (a) the load capacity maximization problem, (b) the weight minimization problem, and (c) coupling the load capacity maximization problem and the weight minimization problem. The potential benefits resulting from the application of reinforcement were evaluated using a combination of finite element analysis and an optimization algorithm based on the differential evolution method. The results demonstrate the significant positive influence of the additional reinforcing members. In particular, the limit load was increased by over 10 times, while the weight was decreased to approximately half. The proposed methodology enabled the development of a substantially improved version of the MB characterized by a higher load capacity and lower weight in comparison to the initial bridge design.

关键词： ultimate loads Optimization algorithms loading capacity Reusability distribution reinforcement Temporary structures scissor differential evolution algorithms MACHINE BASIN Bridge design Safety engineering evolutionary algorithm

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Identification of hysteretic systems using the differential evolution algorithm

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JOURNAL OF SOUND AND VIBRATION 2001年第2期248卷 289-314页

作者： Kyprianou, A Worden, K Panet, M Univ Sheffield Dept Mech Engn Sheffield S1 3JD S Yorkshire England EDF DER MTC F-77818 Moret Sur Loing France

A widely used model in the field of hysteretic or memory-dependent vibrations is that of Bone and Wen. Different parameter values extend its use to various areas of mechanical vibrations. As a consequence an identification method is required to identify the parameter values relevant to its application. Its structure, however, includes internal states and non-linear terms. This rules out the conventional identification methods, such as least squares and maximum likelihood because they require derivative calculations of the prediction error with respect to the parameters. In this paper are presented some results for Bouc-Wen model identification, using simulated noise-free data, simulated noisy data and experimental data obtained from a nuclear power plant. The method used to achieve this is the differential evolution algorithm. differential evolution (DE) is an optimization method developed to perform direct search in a continuous parameter space without requiring any derivative estimation. (C) 2001 Academic Press.

关键词： Error of prediction Hysteresis optimization method direct search differential evolution algorithms Identification methods Mechanical Vibration noisy data evolutionary algorithm parameter space methods maximum likelihood

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differential evolution Algorithm Based Pilot Aided Weighted Data Fusion Scheme for Uplink Base-Station Cooperation System 6

Differential Evolution Algorithm Based Pilot Aided Weighted ...

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6th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Zhang, Zhe Yang, Jing Zhang, Jiankang Mu, Xiaomin Zhengzhou Univ Sch Informat Engn Zhengzhou 450052 Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450000 Peoples R China

ISBN: (纸本)9781479973392

Base Station Cooperation (BSC), has been a promising technique for combating the Inter-Cell Interference (ICI) by exchanging information through a high-speed optical fiber backhaul to increase the diversity gain. In this paper, we propose a novel pilot symbol assisted data fusion scheme for Uplink BSC (UBSC). Furthermore, the proposed scheme exploits the pre-defined pilot symbols as the sample of transmitted symbols to constitute a sub-optimal Weight Calculation (WC) model. To circumvent the non-linear programming problem of the proposed sub-optimal model, differential evolution (DE) algorithm is employed for searching the proper fusion weights. Compared with the existing equal weights based soft combining scheme, the proposed scheme can adaptively adjust the fusion weights according to the accuracy of cooperative information, which remains the relatively low computational complexity and backhaul traffic. Performance analysis and simulation results show that, the proposed scheme can significantly improve the system performance with the pilot settings of the existing standards.

关键词： Bit error rate Computational modeling Data integration Data models Optical wavelength conversion Uplink Vectors up link Optical wavelength conversion data integration data models Bit error rate Computational modeling Pilots differential evolution algorithms Data fusion Weighted data fusion Diversity methods

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Complex Network Analysis of differential evolution Algorithm applied to Flowshop with No-Wait Problem

Complex Network Analysis of Differential Evolution Algorithm...

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2014 IEEE Symposium on differential evolution (SDE)

作者： Davendra, Donald Zelinka, Ivan Metlicka, Magdalena Senkerik, Roman Pluhacek, Michal VSB Tech Univ Ostrava Dept Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic Tomas Bata Univ Zlin Fac Appl Informat Dept Artificial Intelligence Zlin Czech Republic

ISBN: (纸本)9781479944613

This paper analyses the attributes of population dynamics of differential evolution algorithm using Complex Network Analysis tools. The population is visualised as an evolving complex network, which exhibits non-trivial features. Complex network attributes such as adjacency graph gives interconnectivity, centralities give the overview of convergence and stagnation, whereas cliques outlines the depth of interconnection and subgraphs within the population. The community graph plot gives an overview of the hierarchical grouping of the individuals in the population. These attributes give a clear description of the population during evaluation and can be utilised for adaptive population and parameter control.

关键词： evolutionary computation flow shop scheduling graph theory network theory (graphs) FSSNW adjacency graph community graph plot complex network analysis differential evolution algorithm flow shop scheduling with no-wait problem Algorithm design and analysis Complex networks Educational institutions Sociology Statistics Vectors Visualization differential evolution algorithms complex networks evolutionary algorithm Algorithm design and analysis Network analysis Educational institutions Sociology Graph theory Visualization

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Neural network optimization and high-speed railway wheel-set size prediction forecasting based on differential evolution 11

Neural network optimization and high-speed railway wheel-set...

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11th International Conference on Information Optics and Photonics (CIOP)

作者： Zhang, Jiawen Zhang, Yu Luo, Lin Gao, Xiaorong Ling, Zhi Southwest Jiaotong Univ Sch Phys Sci & Technol Chengdu 610031 Peoples R China

ISBN: (数字)9781510631748

ISBN: (纸本)9781510631748

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. In order to grasp the size information of High-speed railway wheel-set size in time and ensure the stable operation of high-speed railway, the size data of wheel-set are obtained by optical intercept image detection, and the LMBP neural network prediction model based on differential evolution is designed and implemented. The differential evolution algorithm (DE) is used to optimize the initial connection weights and thresholds between the layers of the neural network, and solve the problem that the back propagation (BP) network is easy to fall into the local extreme value due to the random initial connection weight and threshold. The Levenberg-Marquardt (LM) numerical algorithm is used to optimize the weights and thresholds in the BP network training process to solve the problem of long BP training time. According to the wheel diameter data of the CRH380 model, the effectiveness and accuracy of the method are verified by comparing the prediction results of different algorithms. Compared with the LMBP network and the standard BP network prediction model, the experimental results show that the DE-LMBP neural network model can obtain better correlation coefficients (0.9974), mean square error (0.0103), mean absolute error (0.0772) and average absolute percentage error (0.0084), which proves that the model is effective in predicting the size of the moving wheel and significantly improves the prediction accuracy.

关键词： differential evolution algorithms prediction model neural network Wheel-set size Levenberg-Marquardt algorithm

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