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Structural Design and Optimization of a Resonant Micro-Accelerometer Based on Electrostatic Stiffness by an Improved differential evolution algorithm

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MICROMACHINES 2022年第1期13卷 38-38页

作者： Huang, Libin Li, Qike Qin, Yan Ding, Xukai Zhang, Meimei Zhao, Liye Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Minist Educ Key Lab Microinertial Instruments & Adv Nav Techn Nanjing 210096 Peoples R China

This study designed an in-plane resonant micro-accelerometer based on electrostatic stiffness. The accelerometer adopts a one-piece proof mass structure;two double-folded beam resonators are symmetrically distributed inside the proof mass, and only one displacement is introduced under the action of acceleration, which reduces the influence of processing errors on the performance of the accelerometer. The two resonators form a differential structure that can diminish the impact of common-mode errors. This accelerometer realizes the separation of the introduction of electrostatic stiffness and the detection of resonant frequency, which is conducive to the decoupling of accelerometer signals. An improved differential evolution algorithm was developed to optimize the scale factor of the accelerometer. Through the final elimination principle, excellent individuals are preserved, and the most suitable parameters are allocated to the surviving individuals to stimulate the offspring to find the globally optimal ability. The algorithm not only maintains the global optimality but also reduces the computational complexity of the algorithm and deterministically realizes the optimization of the accelerometer scale factor. The electrostatic stiffness resonant micro-accelerometer was fabricated by deep dry silicon-on-glass (DDSOG) technology. The unloaded resonant frequency of the accelerometer resonant beam was between 24 and 26 kHz, and the scale factor of the packaged accelerometer was between 54 and 59 Hz/g. The average error between the optimization result and the actual scale factor was 7.03%. The experimental results verified the rationality of the structural design.

关键词： resonant accelerometer electrostatic stiffness structural design differential evolution algorithm

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System Identification of Dual-rate Hammerstein CARARMA Systems by Multi-innovation Improved differential evolution algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2024年第12期22卷 3792-3800页

作者： Zong, Tiancheng Li, Junhong Lu, Guoping Nantong Univ Sch Elect Engn & Automat Nantong 226019 Peoples R China

This study focuses on the parameter estimation of the dual-rate Hammerstein-controlled autoregressive moving average (CARARMA) system. Because dual-rate systems have variables that cannot be directly measured, we use the auxiliary model to obtain a transformed model which can be directly identified. Aiming at the dual-rate Hammerstein CARARMA system, a multi-innovation improved differential evolution identification method is proposed, and its effectiveness is experimentally verified. By comparing with the improved differential evolution algorithm, it can be found that the proposed algorithm can give more accurate parameter estimates.

关键词： Auxiliary model differential evolution algorithm dual-rate sampling Hammerstein systems multi-innovation identification system identification

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Time-varying external archive differential evolution algorithm with applications to parallel mechanisms

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APPLIED MATHEMATICAL MODELLING 2023年 114卷 745-769页

作者： Wen, Shikun Ji, Aihong Che, Linxian Yang, Zhikang Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Lab Locomot Bioinspirat & Intelligent Robots 29 Yudao St Nanjing 210016 Jiangsu Peoples R China Chongqing Vocat Inst Engn Sch Intelligence Mfg & Traff 1 Nanbei Ave Chongqing 402260 Peoples R China

In this work, a time-varying external archive differential evolution algorithm is proposed, which can obtain the multiple roots for system of nonlinear equations for the forward dis-placement of parallel mechanisms. In time-varying external archive differential evolution three improvements are presented. At first, a time-varying subpopulation strategy is pro-posed to consider both global and local searching capabilities. Then, an individual judg-ment criterion is proposed, which employs the weighted basis vector mutation strategy for ordinary individuals and a hybrid mutation operator for locally optimal individuals. Fi-nally, the external archive strategy of neighborhood-based crowding differential evolution is improved to enhance the domain searching ability of the algorithm. To evaluate the per-formance of time-varying external archive differential evolution, twenty benchmark func-tions are selected as the test suite. Moreover, the solutions for forward displacements of four strongly coupled parallel mechanisms with different degrees of freedom are derived to test the generality of this method. From the acquired experimental results, it is demon-strated that fairly-competitive results can be provided compared to other methods, in both benchmark functions and forward displacement solutions of parallel mechanisms. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Parallel mechanism Forward displacement solutions differential evolution algorithm Time-varying subpopulations Individual judgment External archive

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A Novel Closed-Form Expression Obtained by Using differential evolution algorithm to Calculate Pull-In Voltage of MEMS Cantilever

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JOURNAL OF MICROELECTROMECHANICAL SYSTEMS 2018年第3期27卷 392-397页

作者： Ak, Cevher Yildiz, Ali Toros Univ Dept Elect & Elect Engn TR-33140 Mersin Turkey Mersin Univ Dept Elect & Elect Engn TR-33343 Mersin Turkey

In this paper, a novel, computationally efficient, and simple closed-form expression has been derived to accurately calculate the pull-in voltage value of microelectromechanical system (MEMS) cantilever. At first, MEMSs actuators with various physical parameters have been simulated by a software that employs the finite element method, and then, the pull-in voltage expression has been derived by using the differential evolution optimization algorithm together with the simulation data. Since the formula is derived from the simulation data, it implicitly contains the fringing field effect. In order to verify the accuracy and robustness, the predictions of closed-form formula proposed in this paper have been compared with those of the theoretical ones through the simulation and experimental studies previously presented in the literature. The key advantage of the presented model is delivering a satisfying estimation of the pull-in voltage in a simple fashion.

关键词： Cantilever differential evolution algorithm electrostatic actuator MEMS pull-in voltage

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Application of an Adaptive differential evolution algorithm With Multiple Trial Vectors to Artificial Neural Network Training

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2011年第8期58卷 3160-3167页

作者： Slowik, Adam Koszalin Univ Technol Dept Elect & Comp Sci PL-75453 Koszalin Poland

In this paper, an application of an adaptive differential evolution (DE) algorithm with multiple trial vectors for training artificial neural networks (ANNs) is presented. The proposed method is DE-ANNT+, which is a DE-ANN Training (DE-ANNT) modified by adding a multiple trial vectors technique. DE-ANNT+ allows one to train an ANN of arbitrary architectures, and it offers a nondifferentiable neuron activation function. In contrast to a basic DE algorithm, DE-ANNT+ possesses two modifications. In DE-ANNT+, adaptive selection of control parameters and a multiple trial vectors technique are introduced. Adaptive selection means that the number of required parameters of the algorithm is decreased. The multiple trial vectors technique increases the probability of generating a better solution because a greater number of temporary solutions is generated around the existing solutions. The DE-ANNT+ algorithm, with these two modifications, is used for ANN training to classify the parity-p problem. The results from the obtained algorithm have been compared with results from the following algorithms: an evolutionary algorithm, a DE algorithm without multiple trial vectors, gradient training methods, such as error back-propagation, and the Levenberg-Marquardt method.

关键词： Artificial intelligence artificial neural network differential evolution algorithm multiple trial vectors training method

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A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization

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SOFT COMPUTING 2013年第12期17卷 2293-2309页

作者： Kong, Xiangyong Ouyang, Haibin Piao, Xiaoxue Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

In this paper, a new adaptive grouping differential evolution (AGDE) algorithm is proposed to improve the optimization performance by implementing a prediction strategy of the constraints for constrained optimization problems. It is unnecessary to calculate the constraint values when dealing with the constraints in this method. The constraints are handled after a simple prediction according to the Lipschitz condition. When the constraints are very complex, the load arisen from the calculation of the constraint values is reduced dramatically and the feasibility of the solutions remains with great probability. In AGDE algorithm, the population is dynamically grouped to three subpopulations with respective newly-designed mutation strategy. Meanwhile, the mutation factor and crossover probability are adopted associated with the evolutionary process according to the information of the entire population. Both of the above improvements not only increase the diversity of population and speed up the convergence, but also reduce the complexity of the parameter selection. Four sets of comparative experiments are carried out to evaluate the feasibility and effectiveness of the proposed method that deals with the constraints.

关键词： Constrained optimization Prediction of the constraint values differential evolution algorithm Self-adaptive Grouping

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An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10841-10867页

作者： Rauf, Hafiz Tayyab Bangyal, Waqas Haider Khan Lali, M. Ikramullah Univ Gujrat Dept Comp Sci Gujrat Pakistan Univ Educ Dept Informat Sci Lahore Pakistan

differential evolution (DE) is recognized as a simplistic yet robust evolutionary algorithm: it has been utilized to tackle different challenging optimization problems in various science and engineering disciplines. DE has some disadvantages, such as premature convergence and slow convergence rate, leading to the worst DE execution arrangement. Two DE variations named adaptive parameter selection-based DE (APSDE) and chaotic map hybridization based on DE and PSO (CMHDE-PSO) have been proposed to tackle the issues mentioned above. The proposed variants contain three unique advantages for APSDE: (1) new population initialization scheme to keep up the decent variety of the population diversity;(2) controlled mutation factor technique followed by adaptive decreasing parameter selection procedure;(3) novel mutation strategy with a specific weighted pattern to determine the mutant vector for the mutation operation. Similarly, for CMHDE-PSO (1) novel distribution called Torus for the selection of initial population located in the search space;(2) new parameter adoption technique based on chaotic circle maps defined by chaos theory;(3) average pattern means of two different mutation strategies;(4) and lastly the hybridization of proposed improved DE with PSO to supports DE escaping local minima. Both APSDE and CMHDE-PSO are compared with several standard non-DE old-fashioned optimization algorithms and various advanced DE variants. We accomplish definite experiments behind the powerful searching technique by applying the APSDE and CMHDE-PSO-based mutation and parameter selection strategy for the function optimization and weight optimization of feed-forward neural networks (FFNN) on real-world data classification problems. For data classification performance evaluation, 10 data sets are utilized from the repository of UCI machine learning. Experimental results showed that APSDE and CMHDE-PSO extensively beat different EAs in all test functions and obtained higher accuracy wi

关键词： differential evolution algorithm Premature convergence Chaotic maps Adaptive parameter selection

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A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints

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ENERGY 2017年 123卷 538-554页

作者： Zhang, Jingrui Lin, Shuang Liu, Houde Chen, Yalin Zhu, Mingcheng Xu, Yinliang Xiamen Univ Sch Aerosp Engn Dept Instrumental & Elect Engn Xiamen 361005 Peoples R China State Grid Corp China Fujian Elect Power Co LTD Elect Power Res Inst Fuzhou 350007 Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen Engn Lab Geometry Measurement Technol Shenzhen 518055 Peoples R China Sun Yat Sen Univ SYSU CMU Joint Inst Engn Sch Elect & Informat Technol Guangzhou 510275 Guangdong Peoples R China

Short-term optimal hydrothermal scheduling plays one of the most important roles in the modern power system plan and operation. The problem aims at minimizing the total fuel cost of the thermal units while satisfying various constraints such as power balance, water balance and other constraints on thermal units as well as hydro units. Except for the above various constraints, transmission network topology and valve point effects are also introduced into the mathematical optimizing model of the short-term hydrothermal scheduling (STHS) problem. Then a small-population based parallel differential evolution approach is proposed to solve the STHS problem considering power flow constraints. In the proposed approach, a large population is divided into several subpopulations each with a small population size and several parallel running processes of one or more CPUs are performed synchronously each evolving a certain subpopulation and searching for the optimal solution independently. Two different methods are employed in the proposed parallel DE approach in order to avoid low diversity of the small population in each process. One is implemented through the small population itself and the other through the communication mechanism among different running processes. Four constraint handling rules as well as a lead operation are proposed to enhance the feasibility of solutions. Numerical results for two well-known sample test systems are presented to demonstrate the capabilities of the proposed parallel DE algorithm to generate optimal solutions of STHS problem. Two other test systems with transmission networks of standard IEEE 9-bus and IEEE 39-bus are also employed to test the effectiveness of the proposed parallel DE algorithm. The results demonstrate the superiority of the proposed algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal scheduling differential evolution algorithm Parallel Small population Power flow

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Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

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APPLIED SOFT COMPUTING 2017年 61卷 622-641页

作者： Suresh, Shilpa Lal, Shyam Natl Inst Technol Karnataka Dept Elect & Commun Engn Surathkal 575025 Mangaluru India

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified differential evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Image enhancement Metaheuristics differential evolution algorithm Cuckoo search algorithm Satellite images

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Improving sampling-based image matting with cooperative coevolution differential evolution algorithm

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SOFT COMPUTING 2017年第15期21卷 4417-4430页

作者： Cai, Zhao-Quan Lv, Liang Huang, Han Hu, Hui Liang, Yi-Hui Coll Huizhou Huizhou 516007 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China

Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed "sample optimization problem." In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversit) of population. Besides. a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting.

关键词： Image matting Sample selection Cooperative coevolution differential evolution algorithm Diversity of population

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