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ε constrained differential evolution with pre-estimated comparison using gradient-based approximation for constrained optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 44卷 37-49页

作者： Yi, Wenchao Li, Xinyu Gao, Liang Zhou, Yinzhi Huang, Jida Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China SUNY Buffalo Ind & Syst Engn Dept Buffalo NY 14260 USA

Many real-world problems can be categorized as constrained optimization problems. So, designing effective algorithms for constrained optimization problems become more and more important. In designing algorithms, how to guide the individuals moving more efficiently towards the feasible region is one of the most important aspects on finding the optimum of constrained optimization problems. In this paper, we propose an improved constrained differential evolution, which combines with pre-estimated comparison gradient based approximation. The proposed algorithm uses gradient matrix to determine whether the trail vector generated by differential evolution algorithm is worth using the fitness function to evaluate it or not. Pre-estimated comparison gradient based approximation is used as a detector to find the promising offspring and in this way can we guide the individuals moving towards the feasible region. The proposed method is tested both on twenty-four benchmark functions and four well-known engineering optimization problems. Experimental results show that the proposed algorithm is highly competitive in comparing with other state-of-the-art algorithms. The proposed algorithm offers higher accuracy in engineering optimization problems for constrained optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： differential evolution algorithm Constraint optimization problems epsilon level comparison Pre-estimated comparison gradient based approximation

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An Intelligent Optimization Strategy Based on Prediction Model for Carbon Efficiency in Sintering Process

An Intelligent Optimization Strategy Based on Prediction Mod...

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第37届中国控制会议

作者： Jie Hu Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

In order to improve the utilization of carbon in a sintering process, it is necessary to predict and optimize carbon efficiency. In the paper, first, comprehensive coke ratio（CCR） is taken to be a metric of the carbon efficiency. By analyzing the sintering mechanism, sintering parameters affecting the CCR are determined. Next, the fuzzy C-means clustering algorithm is used to identify different operating conditions. Then, least-squares support vector machine（LS-SVM） sub-models are established for the different operating conditions, and a CCR prediction model is established by incorporating the sub-models using the TS fuzzy intelligent fusion method. Finally, based on the CCR prediction model, a differential evolution algorithm is used to optimize the CCR by adjusting the operating parameters. Simulations using actual run data show that the prediction accuracy of the CCR prediction model is higher than that of a back-propagation neural network model and a single LS-SVM model, and the carbon efficiency optimization strategy reduced the CCR by 1.97 kg/t on average. Thus, the method provides us a guidance for an actual sintering process.

关键词： Sintering process Carbon efficiency Fuzzy C-means clustering differential evolution algorithm Least square-support vector machine model

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Research on Target Recognition Based on Improved LSSVM

Research on Target Recognition Based on Improved LSSVM

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第37届中国控制会议

作者： Song Xiao-ru Zeng Jie Gao Song Chen Chao-bo School of Electronic Information Engineering Xi'an Technological University

It is a process that a particular type of goal target recognition from other goals based on certain characteristics. It is one of the hotspots 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 80% to 91%.

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

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Not guaranteeing convergence of differential evolution on a class of multimodal functions

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APPLIED SOFT COMPUTING 2016年 41卷 479-487页

作者： Hu, Zhongbo Su, Qinghua Yang, Xianshan Xiong, Zenggang Yangtze Univ Sch Informat & Math Jingzhou 434023 Hubei Peoples R China Hubei Engn Univ Sch Comp & Informat Sci Xiaogan 432000 Hubei Peoples R China

The theoretical studies of differential evolution algorithm (DE) have gradually attracted the attention of more and more researchers. According to recent researches, the classical DE cannot guarantee global convergence in probability except for some special functions. Along this perspective, a problem aroused is that on which functions DE cannot guarantee global convergence. This paper firstly addresses that DE variants are difficult on solving a class of multimodal functions (such as the Shifted Rotated Ackley's function) identified by two characteristics. One is that the global optimum of the function is near a boundary of the search space. The other is that the function has a larger deceptive optima set in the search space. By simplifying the class of multimodal functions, this paper then constructs a Linear Deceptive function. Finally, this paper develops a random drift model of the classical DE algorithm to prove that the algorithm cannot guarantee global convergence on the class of functions identified by the two above characteristics. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Continuous optimization Convergence in probability differential evolution algorithm Drift analysis

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Quick and Effective Position Control for Planar n-link Underactuated Manipulators Based on Optimization algorithm

Quick and Effective Position Control for Planar n-link Under...

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第37届中国控制会议

作者： Pan Zhang Xuzhi Lai Yawu Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In this paper,a simple one-stage control strategy is developed based on optimization algorithm to quickly realize the position control of a planar n-link（n ≥ 3） underactuated manipulator with a passive first *** continuous control of a planar three-link underactuated manipulator with a passive first joint can be realized,and so can the planar n-link（n ≥ 3） ***,one problem that the number of the variables needing to be optimized will increase with the increase of the number of the links will ***,considering the planar three-link manipulator can be controlled to reach to any target position,we employ the differential evolution（DE） algorithm to optimize the angles of only two active links and the design parameters of the corresponding ***,the angles and the corresponding design parameters of other n — 3 active links are kept being *** this way,all link angles can simultaneously converge to their target *** a planar four-link underactuated manipulator with a passive first joint as an example,the simulation results verify the effectiveness and rapidity of the proposed strategy.

关键词： Planar underactuated manipulators First-order nonholonomic system differential evolution algorithm Position control One-stage control

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A Hybrid Gravitational Search algorithm for Unconstrained Problems

A Hybrid Gravitational Search Algorithm for Unconstrained Pr...

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第30届中国控制与决策会议

作者： Xin Zhang Dexuan Zou Zhi Kong Xin Shen School of Electrical Engineering & Automation Jiangsu Normal University School of Control Engineering Northeastern University at Qinhuangdao

The basic gravitational search algorithm(GSA) could fall into local optima solution easily, and thus we proposed a hybrid gravitational search algorithm(HGSA) in order to overcome the shortcoming of GSA. This improved gravitational search algorithm, which only uses the position update formula that was affected by the Gbest in its iteration process, is combined with the differential evolution(DE) algorithm. Ten benchmark functions have been introduced for testing the improved algorithms' performance. We also use the statistical method "T-test" to verify the difference in results. Experimental results show that HGSA is superior to the basic gravitational search algorithm and its three improved algorithms in terms of both convergence accuracy and convergence rate.

关键词： Gravitational Search algorithm differential evolution algorithm Swarm Intelligence Unconstrained Problems Benchmark Functions

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Image fusion using evolutionary algorithms: a Survey 4

Image fusion using Evolutionary algorithms: a Survey

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4th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Haritha, K. C. Jeyakumar, G. Thangavelu, S. Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Coimbatore Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781509045594

This paper is mainly intended to compare image fusion method using different evolutionary algorithms and a comparison between these methods. The survey focuses on region-based fusion techniques, which is a major area of research. The paper compares image fusion processes using various evolutionary algorithms and illustrates the advantages and disadvantages of these algorithms. This survey illustrates that a method of image fusion can also be included in the DE optimization stage with the block size optimization. Finally, it is concluded that spatial frequency can be used as the sharpness criterion and evolutionary algorithms perform better in block size optimization.

关键词： Image fusion differential evolution algorithm Genetic algorithm Particle Swarm Optimization evolutionary algorithms

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Multi-objective Unit Commitment Model on Wind power Accommodation and Flexible Load Response

Multi-objective Unit Commitment Model on Wind power Accommod...

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2018 International Conference on Data Processing,Artificial Intelligence,and Communications(DPAIC 2018)

作者： Zhengjun Bi Changzheng Gao Benfu Yu Zhong Qin Electric Power Research Institute State Grid Jilin Electric Power Company Limited School of Electrical Engineering Changchun Institute of Technology

Unit commitment is an important aspect of optimal operation of power system. In this paper, the wind power accommodation and flexible load response are proposed to evaluate unit commitment problems. Based on the evaluation functions, the optimization of unit commitment problems is translated into constraints multi-objective optimization problem. These objectives are modeled with a differential evolution algorithm with self-adaptive improved strategies method to evaluate their imprecise nature. The model and algorithms are applied to calculate a case of 10 units. It is shown that the system needs to have more spinning reserve and increase the peak regulation capacity of each time period. The results show that the proposed modeling method can provide a useful guidance for unit commitment problems

关键词： Unit Commitmen Wind power Accommodation Flexible Load Response differential evolution algorithm

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BP Neural Network Based Bearing Fault Diagnosis with differential evolution & EEMD Denoise

BP Neural Network Based Bearing Fault Diagnosis with Differe...

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2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)

作者： Jie Shi Xing Wu Jun Zhou Sen Wang the Faculty of Mechanical & Electrical Engineering Kunming University of Science & Technology the Faculty of Mechanical & Electrical Engineering Yunnan Agricultural University

In mechanical equipment,rolling bearing is frequently *** running state directly affects the performance of the whole machine and it is also the main cause of mechanical equipment *** paper focuses on the fault diagnosis of the rolling *** method of fault diagnosis based on the self-adaptive denoise of ensemble empirical mode decomposition(EEMD) and improved back propagation(BP)neural network with differential evolution(DE) algorithm is ***,the signal is decomposed by EEMD,and then the reconstructed signal of adaptive noise reduction is acquired based on the threshold of distance ***,utilizing the error of BP neural network as the objective function,weights and thresholds of the network are optimized by DE ***,the optimized BP network is used for fault *** results show that the proposed method is more effective and accurate than the traditional BP neural network.

关键词： BP Neural Network differential evolution algorithm EEMD Bearing Fault Diagnosis

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Research on Modeling and Identification for GMA Based on Bragg Grating Sensors

Research on Modeling and Identification for GMA Based on Bra...

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International Conference on Optical and Photonics Engineering (icOPEN)

作者： Shi Bingqing Han Ping Liu Quan Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

ISBN: (数字)9781510610026

ISBN: (纸本)9781510610019;9781510610026

In this paper, a new means of measuring the displacement of GMA (Giant Magnetostrictive Actuator) based on FGB (Fiber Bragg Grating) sensor is proposed, experimental results confirmed that FGB sensor can measure the displacement of GMA in different frequencies and achieve good results. In addition a modified Bouc-Wen model is presented to describe the GMA, the proposed model can describe the asymmetric hysteresis of GMA from 1 Hz to 100 Hz well, and DE(differential evolution) algorithm is used for adaptive identification of the GMA system, the algorithm has fast convergence and high accuracy. Finally, it verifies that the identification model fits the experimental data well.

关键词： FGB sensor Bouc-Wen model differential evolution algorithm

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