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A quick control strategy based on hybrid intelligent optimization algorithm for planar n-link underactuated manipulators

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INFORMATION SCIENCES 2017年 420卷 148-158页

作者： Wang, Yawu Lai, Xuzhi Chen, Luefeng Ding, Huafeng Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China

This paper presents a quick two-stage position control strategy based on a hybrid intelligent optimization algorithm for a planar n-link underactuated manipulator with a passive first joint. In stage 1, the system is directly reduced to a planar virtual Acrobot by controlling n-2 active links to their target angles. A hybrid intelligent optimization algorithm, which includes genetic algorithm (GA) and particle swarm optimization algorithm (PSO), is used to solve all link target angles according to the target position of the system. By coordinating GA and PSO, the hybrid intelligent optimization algorithm ensures that all link target angles, the angle of the passive link at the end of stage 1, and the initial angle of the active link of the planar virtual Acrobot meet the angle constraint of the planar virtual Acrobot. So, the position control objective of the planar n-link underactuated manipulator is realized by controlling the active link of the planar virtual Acrobot to its target angle in stage 2. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Planar underactuated manipulator Genetic algorithm particle swarm optimization algorithm Angle constraint

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Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2017年第4期31卷 1587-1601页

作者： Xiong, Qing Xu, Yanhai Peng, Yiqiang Zhang, Weihua Li, Yongjian Tang, Lan Xihua Univ Key Lab Fluid & Power Machinery Minist Educ Chengdu 610039 Sichuan Peoples R China Xihua Univ Sch Automobile & Transportat Chengdu 610039 Sichuan Peoples R China Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Sichuan Peoples R China

When low-speed rolling bearings fail, it is hard to diagnose the extent of their damage. We developed a test rig to simulate the lowspeed rolling bearing operating condition, where bearings with various fault states are installed on the test wheelset and subjected to the same external loading condition. The collected bearing box acceleration time histories are processed with the Empirical mode decomposition (EMD) method combined with kurtosis criterion to filter the trend and noise components. Five characteristic parameters of Alpha stable distribution (ASD) are identified by fitting the ASD distribution to the vibration acceleration signals and computing the Probability density function (PDF). To highlight the advantage of ASD method in feature extraction, kurtosis also has be calculated. Through sensitivity and stability analysis of the six parameters and utilization of Least squares support vectors machine (LSSVM) with particle swarm optimization (PSO), three most sensitive and stable feature parameters including the characteristic exponent alpha, the scale factor gamma and the peak value of the PDF h are located and applied to evaluate the low-speed rolling bearings' damage position and damage extent. The proposed method was validated by test data, and the results demonstrated that the ASD characteristics combined with PSO-LSSVM can not only achieve fault diagnosis of low-speed rolling bearings' damage position and damage extent, but also have better diagnosis accuracy and operational efficiency than other methods.

关键词： Low-speed rolling bearing Fault diagnosis Empirical mode decomposition Alpha stable distribution particle swarm optimization algorithm Least squares support vector machine

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A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen

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HYDROLOGY RESEARCH 2017年第5期48卷 1214-1225页

作者： Li, Xue Sha, Jian Wang, Zhong-liang Tianjin Normal Univ Tianjin Key Lab Water Resources & Environm Tianjin 300387 Peoples R China

Dissolved oxygen (DO) is an important indicator reflecting the healthy state of aquatic ecosystems. The balance between oxygen supply and consuming in the water body is significantly influenced by physical and chemical parameters. This study aimed to evaluate and compare the performance of multiple linear regression (MLR), back propagation neural network (BPNN), and support vector machine (SVM) for the prediction of DO concentration based on multiple water quality parameters. The data set included 969 samples collected from rivers in China and the 16 predicted variables involved physical factors, nutrients, organic substances, and metal ions, which would affect the DO concentrations directly or indirectly by influencing the water-air exchange, the growth of water plants, and the lives of aquatic animals. The models optimized by particle swarm optimization (PSO) algorithm were calibrated and tested, with nearly 80% and 20% data, respectively. The results showed that the PSO-BPNN and PSO-SVM had better predicted performances than linear regression methods. All of the evaluated criteria, including coefficient of determination, mean squared error, and absolute relative errors suggested that the PSO-SVM model was superior to the MLR and PSO-BPNN for DO prediction in the rivers of China with limited knowledge of other information.

关键词： back propagation neural network multiple linear regression particle swarm optimization algorithm support vector machine water quality parameters

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Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第3期12卷 1027-1037页

作者： Anbarasi, S. Muralidharan, S. Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi India

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named particle swarm optimization (PSO), Bacterial Foraging optimization algorithm (BFOA) and hybrid Bacterial Foraging particle swarm optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

关键词： Bacterial foraging optimization algorithm Hybrid bacterial foraging particle swarm optimization algorithm Load frequency control particle swarm optimization algorithm PID controller

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A social spider optimized image fusion approach for contrast enhancement and brightness preservation

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APPLIED SOFT COMPUTING 2017年 52卷 575-592页

作者： Maurya, Lalit Mahapatra, Prasant Kumar Kumar, Amod CSIR Cent Sci Instruments Org Biomed Instrumentat Div V2 Sect 30C Chandigarh 160030 India Natl Inst Tech Teachers Training & Res Dept Elect & Commun Engn Sect 26 Chandigarh 160019 India

Image enhancement means to process the image in order to obtain a visually pleasing effect on image with more perception details and less noise output. In this work, a social spider optimization based algorithm is used to produce two enhanced images, one having high sharpness or contrast with maximum entropy and the other having high peak signal-to-noise ratio (PSNR) sharp image. The two enhanced images are fused to obtain an output image which has an optimal trade-off between sharpness and noise. The proposed algorithms are applied to lathe tool images as well as to some other standard images to verify their effectiveness. The results are compared with conventional image enhancement techniques such as Histogram Equalization (HE), Linear Contrast Stretching (LCS), and Standard particle swarm optimization (PSO) algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Social spider optimization particle swarm optimization algorithm Image enhancement Image fusion Histogram equalization Linear contrast stretching

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Clustering performance comparison of new generation meta-heuristic algorithms

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KNOWLEDGE-BASED SYSTEMS 2017年第Aug.15期130卷 1-16页

作者： Ozbakir, Lale Turna, Fatma Erciyes Univ Ind Engn Dept TR-38039 Kayseri Turkey

This article addressed two new generation meta-heuristic algorithms that are introduced to the literature recently. These algorithms, proved their performance by benchmark standard test functions, are implemented to solve clustering problems. One of these algorithms called Ions Motion optimization and it is established from the motions of ions in nature. The other algorithm is Weighted Superposition Attraction and it is predicated on two fundamental principles, which are "attracted movements of agents" and "superposition". Both of the algorithms are applied to different benchmark data sets consisted of continuous, categorical and mixed variables, and their performances are compared to particle swarm optimization and Artificial Bee Colony algorithms. To eliminate the infeasible solutions, Deb's rule is integrated into the algorithms. The comparison results indicated that both of the algorithms, Ions Motion optimization and Weighted Superposition Attraction, are competitive solution approaches for clustering problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Ions motion algorithm Weighted superposition attraction algorithm particle swarm optimization algorithm Artificial bee colony algorithm

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Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

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JOURNAL OF ELECTRICAL SYSTEMS 2017年第2期13卷 285-294页

作者： Abdolrasol, Maher G. M. Mohamed, Azah Hannan, M. A. Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Tenaga Nas Coll Engn Dept Elect Power Engn Bandar Baru Bangi 43650 Selangor Malaysia

This paper discuss virtual power plant (VPP) and Microgrid controller for energy management system (EMS) based on optimization techniques by using two optimization techniques namely Backtracking search algorithm (BSA) and particle swarm optimization algorithm (PSO). The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.

关键词： Virtual Power Plant Microgrid Energy Management System Backtracking search algorithm particle swarm optimization algorithm

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The Berth-Quay Cranes and Trucks Scheduling optimization Problem by Hybrid Intelligence Swam algorithm

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2017年第2期11卷 74-89页

作者： Liu, Yi Shahbazzade, Sabina Hangzhou Dianzi Univ Sch Management Hangzhou Zhejiang Peoples R China Univ Calif Berkeley Elect Engn & Comp Sci Berkeley CA 94720 USA

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. The proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.

关键词： Artificial Fish swarm algorithm Bioinformatics Computational Intelligence Container Truck Scheduling Problem particle swarm optimization algorithm Quay Crane Scheduling Problem

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Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering

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DECISION SUPPORT SYSTEMS 2010年第4期49卷 451-462页

作者： Kuo, R. J. Lin, L. M. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan AU Optron Corp Hsinchu 300 Taiwan

This study proposes an evolutionary-based clustering algorithm based on a hybrid of genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) for order clustering in order to reduce surface mount technology (SMT) setup time. Simulational results via Iris, Glass, Vowel and Wine benchmark data sets indicate that the proposed evolutionary-based clustering algorithm is more accurate than the GA-based and PSOA-based clustering algorithms. In addition, the model evaluation results which use order information provided by an international industrial personal computer (PC) manufacturer show that the proposed algorithm is also superior to GA-based and PSOA-based clustering algorithms. Through order clustering, scheduling orders that belong to the same cluster together can reduce production time as well as machine idle time. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Clustering analysis ART2 neural network particle swarm optimization algorithm Genetic algorithm

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Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

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INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING 2017年第4期8卷 331-341页

作者： Bechouat, Mohcene Younsi, Abdelaziz Sedraoui, Moussa Soufi, Youcef Yousfi, Laatra Tabet, Ismail Touafek, Khaled Univ 8 Mai 1945 Guelma Lab Telecommun Guelma Algeria Univ Tebessa Labget Lab Dept Elect Engn Tebessa Algeria Univ Tebessa Lab Inverses Problems Modeling Informat & Syst PI Tebessa Algeria CDER URAER Ghardaia 47133 Algeria

Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms;genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.

关键词： Photovoltaic system Photovoltaic thermal system Modelization Identification Genetic algorithm particle swarm optimization algorithm

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