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37th Chinese Control Conference (CCC)

作者： Song Xiao-ru Zeng Jie Gao Song Chen Chao-bo Xian Technol Univ Sch Elect Informat Engn Xian 710021 Shaanxi Peoples R China

ISBN: (纸本)9789881563958

It is a process that a particular type of goal target recognition from other goals based on certain characteristics. It is one of the hot spots of current research. LSSVM is a common algorithm for target recognition. It is simple, fast and accurate. Against to the parameters difficult to be determined in the LSSVM and the disadvantage of traditional method, it is proposed an improved differential evolution algorithm to optimize the parameters of the LSSVM in the paper. To restrain the premature convergence of the traditional algorithm, the premature judgment mechanism is introduced by the way of improving the mutation strategy. The experiment results showed that the improved algorithm has the ability of jumping out of local advantages. The results are better than the traditional algorithm. it is proved with the photoelectric servo tracking turntable that the improved algorithm has fast convergence speed and high accuracy. And it is demonstrated the excellence of the algorithm that correct recognition rate can be improved from 50% to 91%.

关键词： differential evolution algorithm squares support vector machine target recognition

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Optimizing product manufacturability in 3D printing

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Frontiers of Computer Science 2017年第2期11卷 347-357页

作者： Yu HAN Guozhu JIA School of Economics and Management Beihang University Beijing 100191 China

3D printing has become a promising technique for industry production. This paper presents a research on the manufacturability optimization of discrete products un- der the influence of 3D printing technology. For this, we first model the problem using a tree structure, and then formulate it as a linear integer programming, where the total production time is to be minimized with the production cost constraint. To solve the problem, a differential evolution （DE） algorithm is developed, which automatically determines whether tra- ditionai manufacturing methods or 3D printing technology should be used for each part of the production. The algorithm is further quantitatively evaluated on a synthetic dataset, com- pared with the exhaustive search and alternating optimization solutions. Simulation results show that the proposed algo- rithm can well combine the traditional manufacturing meth- ods and 3D printing technology in production, which is help- ful to attain optimized product design and process planning concerning manufacture time. Therefore, it is beneficial to provide reference of the widely application and further in- dustrialization of the 3D printing technology.

关键词： 3D printing manufacturability optimization,discrete products differential evolution algorithm

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State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO₄ batteries

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2018年第8期42卷 2710-2727页

作者： Xie, Jiale Ma, Jiachen Bai, Kun Harbin Inst Technol Sch Astronaut Harbin Heilongjiang Peoples R China State Grid Jibei Elect Power Co Ltd Maintenance Branch Beijing Peoples R China

To achieve accurate state-of-charge (SoC) estimation for LiFePO4 batteries, the effects of temperature, hysteresis, and thermal evolution are elaborately modeled. Open-circuit voltage is regarded as the sum of electromotive force and hysteresis potential (="mathematics"V-h), where electromotive force is constructed as the function of SoC and temperature and ="mathematics">V-h is reproduced with a geometrical model. By simulating battery heat generation and dissipation, a thermal evolution model is established and exploited for open-circuit voltage and parameter identification. Then, on the basis of a second-order equivalent circuit model, 2 SoC estimation schemes are proposed: One scheme uses the recursive least square with forgetting factor algorithm and off-line equivalent circuit model parameters derived by the differential evolution algorithm;the other scheme resorts to the adaptive extended Kalman filter (EKF) and online tuned parameters. Experiments validate the effectiveness of the hysteresis model and the thermal evolution model. In contrast to a joint EKF estimator, experimental results under different temperatures and initial states suggest that both the proposed estimators are superior to the joint EKF estimator. Benefiting from the online updated parameters, the adaptive EKF estimator behaves best for giving consistent SoC-tracking performance under different conditions.

关键词： adaptive extended Kalman filter differential evolution algorithm hysteresis model parameter identification state of charge thermal evolution model

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Competitive evolutionary algorithms for building performance database of a microwave transistor

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2018年第2期46卷 244-258页

作者： Gunes, Filiz Belen, Mehmet A. Mahouti, Peyman Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey Artvin Coruh Univ Dept Elect & Elect Engn Artvin Turkey

In this work, the simultaneous trade-off relations among the noise figure F, gain G(T), input V-in, and output V-out VSWRs of a microwave transistor operated at a certain (V-DS, I-DS, f) condition are obtained fast and as accurate as the corresponding analytical results using multiobjective optimization process without any need for expertise on the microwave device, circuit, and noise. Three powerful evolutionary algorithms, cuckoo search, firefly, and differential evolution, are implemented comparatively as a study case to obtain the trade-off relations of a typical low-noise amplifier transistor NE3511S02 for its operation between 9 and 17GHz at V-DS=2V and I-DS=10mA. Finally, differential evolution is found as the most successful algorithm to demonstrate the typical trade-off relations of NE3511S02. It can be concluded that these trade-off relations being obtained by using a signal and noise model of the transistor enable performance database covering all the (FFmin, G(T), V(in)1, V(out)1) quadruples with their (Z(S), Z(L)) termination pairs using solely an evolutionary optimization process. Thus, a small signal transistor can be identified by its performance database to be used in the design optimization of high-performance low-noise amplifiers with the full device capacity.

关键词： microwave transistor multiobjective optimization competitive evolutionary algorithms low-noise amplifier (LNA) cuckoo search algorithm firefly algorithm differential evolution algorithm

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Hybrid Task Scheduling Method for Cloud Computing by Genetic and DE algorithms

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WIRELESS PERSONAL COMMUNICATIONS 2017年第4期97卷 6301-6323页

作者： Kamalinia, Amin Ghaffari, Ali Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

Cloud computing is a new technology which provides online services to the consumers. In order to have a high efficiency in cloud computing, proper task scheduling is required. Since the task scheduling in cloud computing is regarded as an NP complete problem, so traditional heuristic algorithms do not have the required efficiency in this environment. Therefore, recently, the majority of the proposed task scheduling algorithms have focused on hybrid meta-heuristic methods for task scheduling. In this paper, we proposed a hybrid meta-heuristic method by using HEFT algorithm. The obtained results of the simulation and statistical analysis revealed that the proposed algorithm outperforms three other heuristic and genetic algorithms in terms of the makespan in the randomly Direct Acyclic Graphs (DAGs).

关键词： Cloud computing Task scheduling Genetic algorithm differential evolution algorithm

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Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2018年第2期47卷 605-614页

作者： Dunder, Emre Gumustekin, Serpil Murat, Naci Cengiz, Mehmet Ali Ondokuz Mayis Univ Fac Sci Dept Stat Samsun Turkey Ondokuz Mayis Univ Fac Engn Dept Ind Engn Samsun Turkey

In statistical analysis, one of the most important subjects is to select relevant exploratory variables that perfectly explain the dependent variable. Variable selection methods are usually performed within regression analysis. Variable selection is implemented so as to minimize the information criteria (IC) in regression models. Information criteria directly affect the power of prediction and the estimation of selected models. There are numerous information criteria in literature such as Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). These criteria are modified for to improve the performance of the selected models. BIC is extended with alternative modifications towards the usage of prior and information matrix. Information matrix-based BIC (IBIC) and scaled unit information prior BIC (SPBIC) are efficient criteria for this modification. In this article, we proposed a combination to perform variable selection via differential evolution (DE) algorithm for minimizing IBIC and SPBIC in linear regression analysis. We concluded that these alternative criteria are very useful for variable selection. We also illustrated the efficiency of this combination with various simulation and application studies.

关键词： differential evolution algorithm Information criteria Linear regression Optimization Variable selection

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Structural damage diagnosis with uncertainties quantified using interval analysis

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STRUCTURAL CONTROL & HEALTH MONITORING 2017年第10期24卷 1-N.PAG页

作者： Liu, Gang Mao, Zhu Chongqing Univ Minist Educ Cities Mt Area Key Lab New Technol Construct Chongqing Peoples R China Chongqing Univ Sch Civil Engn Chongqing Peoples R China Univ Massachusetts Lowell Dept Mech Engn Lowell MA USA

The in situ structural assessment by means of structural health monitoring (SHM) has received a great attention in all sorts of civil engineering applications. However, SHM implementations especially damage detections for real-world infrastructures are always overwhelmed with uncertainties of high dimensionality. A nonprobabilistic uncertainty-quantification-enhanced damage diagnosis method is proposed in this study with respect to interval analysis on SHM features. The diagonal elements of the vector auto-regressive model, constructed from the data measurements, are firstly extracted to form a vector, and this vector's Mahalanobis distance between pristine and unknown conditions is used as a damage-sensitive feature. Subsequently, the uncertainty sources, such as measurement inaccuracy and physical variability, are considered as influencing variables. A differential evolution algorithm is thereby introduced to convert the fluctuating interval of those variables into the uncertainty interval of Mahalanobis distance estimation. Finally, inspired by the idea of receiver operating characteristics when probability of detection is available, a modified mathematic metric is defined suited for interval analysis, and area under the modified receiver operating characteristics curve is employed to detect and localize damages. A contrived numerical mass-spring system and a laboratory-scale frame structure are used to validate the proposed framework;and in addition, the damage severity is able to be quantified via a proposed interval distance between pristine and inspection conditions.

关键词： damage detection differential evolution algorithm interval analysis receiver operating characteristics structural health monitoring uncertainty quantification

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evolutionary Pareto optimization of an ANFIS network for modeling scour at pile groups in clear water condition

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FUZZY SETS AND SYSTEMS 2017年 319卷 50-69页

作者： Azimi, Hamed Bonakdari, Hossein Ebtehaj, Isa Talesh, Seyed Hamed Ashraf Michelson, David G. Jamali, Ali Razi Univ Dept Civil Engn Kermanshah Iran Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Univ Guilan Dept Mech Engn Rasht Iran

The existence of waves and currents around the foundations of pile groups causes scour and reduces the stability of structures in coastal environments and rivers. In this study, a new hybrid multi-objective technique is developed to predict scour at pile groups in clear water condition. Singular Decomposition Value (SVD) and the differential evolution (DE) algorithm are used to determine the linear consequent parameters and nonlinear antecedent parameters, respectively, in an Adaptive Neuro-Fuzzy Inference System (ANFIS). Furthermore, the Pareto curve is used to select the tradeoff between two different objective functions, namely training error (TE) and prediction error (PE) to consider the flexibility of modeling and to optimally design the proposed method (ANFIS-DE/SVD). First, the most effective parameters on predicting scour at pile groups are determined and modeled by ANFIS-DE/SVD. Subsequently, sensitivity analysis is carried out using ANFIS-DE/SVD to identify the effect of each parameter on predicting scour at pile groups. The results indicate that the best input combination for estimating scour at pile groups includes critical velocity due to the incipient motion of sediment on the bed, mean particle diameter, flow depth, pile diameter, center-to-center distance between adjacent piles in line with the flow, center-to-center distance between adjacent piles perpendicular to the flow, number of piles parallel to the flow and number of piles normal to the flow. According to the sensitivity analysis results, the pile diameter and number of piles normal to the flow are the most effective parameters for predicting scour at pile groups in clear water condition. Furthermore, the ANFIS-DE/SVD results for the superior input combination are compared with the genetic algorithm and empirical equations. The results indicate that ANFIS-DE/SVD outperforms the other techniques. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Pile group Scour depth ANFIS differential evolution algorithm Pareto curve Singular value decomposition

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Fractional order PI^λD^μ controller design for satisfying time and frequency domain specifications simultaneously

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ISA TRANSACTIONS 2017年 68卷 212-222页

作者： Zheng, WeiJia Luo, Ying Wang, XiaoHong Pi, YouGuo Chen, YangQuan Foshan Univ Sch Automat Foshan Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou Guangdong Peoples R China Univ Calif Merced Sch Engn 5200 North Lake Rd Merced CA 95343 USA

In order to achieve a desired control performance characterized by satisfying specifications in both frequency-domain and time-domain simultaneously, an optimal fractional order proportional integral derivative ((PID mu)-D-lambda) controller design strategy is proposed based on analytical calculation and differential evolution algorithm for a permanent magnet synchronous motor(PMSM) servo system in this paper. In this controller design, the frequency-domain specifications can guarantee the system stability with both gain margin and phase margin, and also the system robustness to loop gain variations. The time-domain specifications can ensure the desired step response performance with rapid rising curve, constrained overshoot, and proper power consuming. Compared with the PI lambda controller and the traditional PID controller, (PID mu)-D-lambda controller can get obvious benefits from two more degrees of freedom of the fractional orders lambda and mu on satisfying multiple constraints simultaneously and achieving better servo tracking performance for the PMSM servo system. PMSM speed tracking simulations and experiments are demonstrated to show the significant advantages of using the proposed optimal (PID mu)-D-lambda controller over the optimal fractional order PI lambda controller and traditional integer order PID controller. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Fractional order (PID mu)-D-lambda controller design PMSM servo system differential evolution algorithm

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A surrogate based multi-fidelity approach for robust design optimization

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APPLIED MATHEMATICAL MODELLING 2017年 47卷 726-744页

作者： Chakraborty, Souvik Chatterjee, Tanmoy Chowdhury, Rajib Adhikari, Sondipon Indian Inst Technol Roorkee Dept Civil Engn Roorkee Uttar Pradesh India Swansea Univ Coll Engn Singleton Pk Swansea SA2 8PP W Glam Wales

Robust design optimization (RDO) is a field of optimization in which certain measure of robustness is sought against uncertainty. Unlike conventional optimization, the number of function evaluations in RDO is significantly more which often renders it time consuming and computationally cumbersome. This paper presents two new methods for solving the RDO problems. The proposed methods couple differential evolution algorithm (DEA) with polynomial correlated function expansion (PCFE). While DEA is utilized for solving the optimization problem, PCFE is utilized for calculating the statistical moments. Three examples have been presented to illustrate the performance of the proposed approaches. Results obtained indicate that the proposed approaches provide accurate and computationally efficient estimates of the RDO problems. Moreover, the proposed approaches outperforms popular RDO techniques such as tensor product quadrature, Taylor's series and Kriging. Finally, the proposed approaches have been utilized for robust hydroelectric flow optimization, demonstrating its capability in solving large scale problems. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Robust design optimization Polynomial correlated function expansion differential evolution algorithm Stochastic computation

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