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2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES)

作者： Shih, Chien-Jong Teng, Tso-Liang Chen, Shiau-Kai Tamkang Univ Dept Mech & Electromech Engn Danshui 25137 New Taipei Taiwan Hsiuping Univ Sci & Technol Dept Mech Engn Taichung 41280 Taiwan

ISBN: (纸本)9783319045733;9783319045726

A niche-related particle swarm meta-heuristic algorithm for dealing with multimodal optimization problem is proposed in this paper. The inspiration and numerical algorithm are presented and the Rastrigin function with numerous local optima is adopted as the illustrative example. Proposed multimodal particle swarm optimization (MPSO) is sensitive to predetermined multimodal numbers, particle numbers, niche radius, and convergent iterations. The results show that the proposed MPSO is accurate and stable. The presented MPSO is ready for applied engineering optimization and further application.

关键词： particle swarm optimization algorithm Multimodal function Niche Bio-logical based optimization

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A new data classification method based on chaotic particle swarm optimization and least square-support vector machine

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2015年 147卷 147-156页

作者： Liu, Fang Zhou, Zhiguang Zhejiang Univ Finance & Econ Inst Informat Hangzhou 310018 Zhejiang Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Guangxi Univ Nationalities Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China

In order to improve the classification accuracy in chemometrics data, chaotic optimization algorithm (COA) and particle swarm optimization (PSO) algorithm are introduced into least square-support vector machine (LS-SVM) model in order to propose an optimized LS-SVM model and a novel data classification (CPL-SVM) method in this paper. In the proposed CPL-SVM method, the COA with the randomness and ergodicity is used to chaotically process the initial position and local extreme position of parade in the PSO algorithm in order to obtain a chaotic particle swarm optimization (CPSO) algorithm, and the CPSO is used to select and optimize the important parameters of LS-SVM, then the optimized parameters are used to obtain a better CPL-SVM classification method. The choice randomness of parameters is avoided and the selection workload of parameters is reduced. And this method can not only overcome the time-consuming and blindness of cross validation method, but also reflect small sample learning ability. In order to verify the effectiveness of CPL-SVM method, binary classification data, IRIS flower data and three relevant data sets with pharmacodynamic properties of drug are selected in this paper. The experiment results show that the proposed CPL-SVM method takes on the better learning performance, strong generalization ability, best sensitivity, Matthews correlation coefficient and classification accuracy. And it can effectively avoid the isolated effects of sample in the learning process. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Chaotic optimization algorithm particle swarm optimization algorithm Least square-support vector machine (LS-SVM) Data classification Generalization ability Classification accuracy

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Modelling of gas to hydrate conversion for promoting CO₂ capture processes in the oil and gas industry

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第7期34卷 642-651页

作者： Baghban, Alireza Bahadori, Mohammad Kashiwao, Tomoaki Bahadori, Alireza Petr Univ Technol Ahwaz Fac Petr Engn Dept Gas Engn Ahvaz Iran Griffith Univ Griffith Sch Environm Nathan Campus Nathan Qld 4111 Australia Natl Inst Technol Dept Elect & Control Engn Niihama Japan So Cross Univ Sch Environm Sci & Engn Lismore NSW 2480 Australia

In this contribution, equilibrium conditions of clathrate hydrates containing mixtures of carbon dioxide and tetra-n butylammonium bromide and tetra-n butylammonium chloride are modeled by a combination of particle swarm optimization algorithm with least square support vector machine intelligent approach. The evaluation of aforementioned model has been conducted by statistical analyses between experimental and predicted values. The obtained results show excellent performance of proposed model.

关键词： Semiclathrate hydrate CO2 TBAC TBAB promoter particle swarm optimization algorithm least square support vector machine

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Multi-Objective optimization Model for the Allocation of Water Resources in Arid Regions Based on the Maximization of Socioeconomic Efficiency

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WATER RESOURCES MANAGEMENT 2016年第3期30卷 927-946页

作者： Davijani, M. Habibi Banihabib, M. E. Anvar, A. Nadjafzadeh Hashemi, S. R. Univ Tehran Univ Coll Abureyhan Dept Irrigat & Drainage Tehran Iran Politecn Milan Univ Dept Civil & Environm Engn Milan Italy Univ Birjand Dept Water Resources Engn Birjand Iran

The escalating world population has led to a drastic increase in water demand in the municipal and drinking water, agriculture and industry sectors. This situation necessitates application of effective measures for the optimal and efficient management of water resources. With this respect, a two-objective socioeconomic model (aimed at job creation) has been presented in this study for the optimum allocation of water resources to industry, agriculture and municipal water sectors. In the agriculture sector, the production function of each product has been determined and then, based on the production functions, areas under cultivation, product yield and the income obtained from each product, the combined objective function has been specified. In the industry sector, since water demand is a function of the amount of produced products, price of supplied water and the price of other supplies, the demand function of this sector was determined regionally. Also, considering the existing necessity in meeting the municipal water requirement, the total amount of water needed by this sector was fully allocated. Then by using two meta-heuristic algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO), the objective functions were maximized and the water resources were optimally allocated between agriculture and industry sectors and the results

关键词： Allocation of water resources Job creation Agriculture and industry Two-objective optimization Genetic algorithm particle swarm optimization algorithm

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Balancing and positioning for a gun control system based on fuzzy fractional order proportional-integral-derivative strategy

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ADVANCES IN MECHANICAL ENGINEERING 2016年第3期8卷 1-9页

作者： Gao, Qiang Li, Kang Hou, Yuanlong Hou, Runmin Wang, Chao Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China

To achieve perfect behavior of the unbalanced barrel of a gun control system, a novel control strategy for simultaneous balancing and positioning of the system is proposed, physically being on the basis of a novel hydraulic cylinder with three cavities. The fuzzy fractional order proportional-integral-derivative controller is developed, and the particle swarm optimization algorithm is adopted for optimal selection of the control parameters for the gun control system. The results demonstrate that the fuzzy fractional order proportional-integral-derivative control strategy can finely improve dynamic performance of the control system, and the nonlinear characteristics of system can be effectively suppressed.

关键词： Gun control system balancing and positioning fuzzy fractional order proportional-integral-derivative particle swarm optimization algorithm

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optimization of Reactive Power Based on Dynamic Learning Factor Multi-objective particle swarm algorithm

Optimization of Reactive Power Based on Dynamic Learning Fac...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Ai, Ying Su, Yixin Peng, Yao Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9783038351153

particle swarm optimization algorithm has the defects of easy to fall into local optimum and low convergence accuracy used in reactive power optimization. To solve the problems, this paper proposed an improved particle swarm optimization algorithm based on dynamic learning factors. The two accelerations are changed with searching stage, so as to enhance the early globle search ability and the late local search ability, then to avoid local optimum;minimum particle angle method and crowded distance method are uesd to determine the global extremum in instalments, so as to improve the convergence speed and accuracy of multi-objective pareto solutions. Take the IEEE 30 bus system IEEE 118 bus system as example, the proposed method is compared with adaptive chaos particle swarm optimization (ACPSO) and NSGA-II, simulation results show that the method put forward in this paper has better convergence accuracy.

关键词： Dynamic Learning Factor particle swarm optimization algorithm Minimum particle Angle Method Crowded Distance Method Reactive Power optimization

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Optimized sensor and sensorless control of PMSM modeled in discrete mode A comparison of KH and PSO algorithms

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2016年第3期35卷 1293-1320页

作者： Younesi, Aria Kazemi, Yousef Moradpour, Arman Tohidi, Sajjad Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Purpose - The purpose of this paper is to improve dynamic performance of the permanent magnet synchronous motor (PMSM) field-oriented control by means of optimizing its PI controller's coefficients. Design/methodology/approach - In this paper, Krill Herd (KH) optimization algorithm is proposed to optimize PI controller coefficients. Moreover, drive control system and steady state estimator are implemented in discrete mode. In order to evaluate the performance of the proposed algorithm, a comparison is carried out between KH and particle swarm optimization (PSO) algorithms. It is noteworthy that PSO algorithm has presented the best dynamic response among the studied approaches in literature. Findings - By using the proposed optimization algorithm, the system tracks the reference speed with a very low error. In addition, the discrete mode simulations do not require initiating integrators. Moreover, it significantly increases the processing speed. The proposed approach is carried out in both sensor and sensorless drive systems. The simulation results indicate lower torque ripple, shorter settling time, lower overshoot, faster dynamic response and higher robustness against speed, torque and machine parameter changes. Practical implications - The PI controller is extensively used in practical drive systems, due to its simple structure and implementation as well as its acceptable performance. On the other hand, PMSM benefits from high-power density, high efficiency, low torque ripple, low inertia, and high performance in a wide speed range. This paper presents an efficient approach to optimize PI coefficients of the PMSM drive system. Originality/value - This paper proposes a new algorithm (KH algorithm) to determine the coefficients of PI controllers in order to improve dynamic performance of the PMSM drive system.

关键词： Permanent magnet synchronous motor Field-oriented control Krill Herd optimization algorithm particle swarm optimization algorithm PI controller

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A Short-Term Traffic Flow Prediction Model Based on EMD and GPSO-SVM

A Short-Term Traffic Flow Prediction Model Based on EMD and ...

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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Mei Duo Yan Qi Gao Lina E Xu College of Information Science and Technology Bohai University

According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition(EMD), genetic particle swarm optimization(

ISBN: (纸本)9781467389808

关键词： Traffic flow prediction Empirical model decomposition Genetic algorithm particle swarm optimization algorithm Support vector machine

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Optimal Active Power Loss with Feeder Routing Collaborate Distributed Generation Allocation and Sizing in Smart Grid Distribution

Optimal Active Power Loss with Feeder Routing Collaborate Di...

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2017经济金融与统计国际会议

作者： Phatcharasak Phawanaphinyo Narongdech Keeratipranon Chaiyaporn Khemapatapan College of Innovative Technology and Engineering Dhurakij Pundit University

The Smart Grid(SG) has widely supported the electric power from the Distributed Generation(DG). It becomes a practical standard to generate the electric power from a renewable energy into the distribution system to compensate for the power demand, especially in the peak time. However, the advancement of the SG continuing with the classical problem of active power loss as the traditional grid. This research aimsto solve the active power loss problem by analyzing the elements and study to solve, in the scope of the organization that provides the electrical power. In order to solve the problem, the solutions can be achieved by feeder routing with adjusting cost Dijkstra's algorithm, afterward decided the allocation and sizing of DG by using the Evolutionary Computing(EC) which are Harmony Search(HS), Artificial Bee Colony(ABC), and particle swarm optimization(PSO) algorithms. The experiment evaluates the performance of the algorithm usingpower flow analysis, Backward/Forward Sweep Method, on the IEEE 33-bus system. From the experimental result, the PSO provides the best performance. The overall active power loss in the case 3 DGs was reduced from 202.67 to 52.29 k W, representing 74.20%reduction.

关键词： Minimal active power loss Backward\Forward Sweep Method Harmony Search algorithm Artificial Bee Colony algorithm particle swarm optimization algorithm

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Application of Improved Wavelet Neural Network in MBR Flux Prediction

Application of Improved Wavelet Neural Network in MBR Flux P...

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International Conference on Computer and Information Science

作者： Guoshuai Cai Chunqing Li College of Computer Science and Software Tianjin Polytechnic University No. 399 Bin Shui Xi Road Xi Qing District Tianjin 300387 P.R. of China

ISBN: (纸本)9781509055081

Membrane Bio-Reactor(MBR) technology plays an important role in modern sewage treatment, but the performance of the MBR technology is seriously affected by the membrane fouling. In general, the result of membrane fouling is decline of MBR membrane flux, and the effect of MBR sewage treatment is directly affected by the decrease of membrane flux. In order to predict MBR membrane flux accurately and rapidly, the forecasting model of MBR membrane flux based on particle swarm improving wavelet neural network algorithm (PSO_WNN) was established. In view of the complexity of the MBR membrane fouling factor, in the beginning,the main components of the factors affecting the flux of MBR membrane were analyzed .The important factor is extracted as the input of the PSO_WNN prediction model, and the membrane flux is used as the ***, the PSO_WNN simulation model is established, and the prediction results are obtained by using the model. By comparing the predicted data and experimental data, the predictive accuracy of this algorithm is high on the membrane flux, and compared with the BP neural network model, the comparative results show that the PSO_WNN forecasting model has higher predicted accuracy.

关键词： MBR Membrane flux particle swarm optimization algorithm Wavelet neural network BP neural network

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