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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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Blending Artificial Intelligence into PID Controller Design: A Biomedical Engineering Experiment

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IFAC-PapersOnLine 2016年第6期49卷 366-371页

作者： Oliveira, P. B. de Moura Cunha, J. Boaventura UTAD-University of Trás-os-Montes and Alto Douro Escola de Ciencias e Tecnologia Vila Real5000-801 Portugal INESC TEC Technology and Science Campus da FEUP Porto4200 - 465 Portugal

A teaching experiment is proposed in which an artificial intelligence technique is blended with classical control techniques to design PID controllers. The artificial intelligence technique deployed is currently considered one of the most popular and successfully nature and biological inspired metaheuristics: the particle swarm optimization algorithm. The teaching experiment is proposed for an introductory undergraduate Biomedical Engineering feedback control systems course. The mean arterial pressure control, quite relevant in practical application terms, is revisited. Moreover, another biomedical control problem is proposed for teaching/learning purposes: the minimum temperature control for intracranial tumor treatment. Simulation results concerning both classic and artificial intelligence based techniques for PID controller design are presented. © 2016

关键词： Controllers Adaptive control systems Artificial intelligence Bioinformatics Biomedical engineering Biophysics Blending Blood pressure Design Electric control equipment Feedback control particle swarm optimization (PSO) Proportional control systems swarm intelligence Teaching Three term control systems Tumors Artificial intelligence techniques Biomedical control Classical control techniques Intracranial tumors Mean arterial pressure Minimum temperatures particle swarm optimization algorithm PID controller design

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Solving Multi-objective Portfolio optimization Problem for Saudi Arabia Stock Market Using Hybrid Clonal Selection and particle swarm optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015年第8期40卷 2407-2421页

作者： Bin Shalan, Sara A. Ykhlef, Mourad King Saud Univ Dept Nat & Engn Sci Coll Appl Studies & Community Serv Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

The portfolio is a group of assets held by an institution or a private individual. Each asset has an investment share of the total investment. The investor tries to distribute the investment to these different assets. The main issue in portfolio optimization is the allocation of different assets for maximum return and minimum risk within a suitable time. These two objectives lead to the multi-objective portfolio optimization problem, which must be solved. Several previous studies have addressed this issue. In this article, we propose a new intelligence hybrid evolutionary algorithm that combines clonal selection with particle swarm optimization to optimize the portfolio's return and risk. We then show the results of the proposed solution through experiments that are conducted using stocks in Kingdom of Saudi Arabia stock exchange market (Tadawul). Moreover, we compare our hybrid algorithm, clonal selection and particle swarm optimization-based solution.

关键词： Clonal selection algorithm Hybrid evolutionary algorithm Portfolio optimization particle swarm optimization algorithm

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A New algorithm for Solving Ill-conditioned Linear System with an Auto-Parameter

A New Algorithm for Solving Ill-conditioned Linear System wi...

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Tianyu He Jinshan Wang Zonghao Tian Mathematics section Army Officer Academy

ISBN: (纸本)9781510845008

A new simple and effective method for solving ill-conditioned linear systems is presented in this paper. This method tried mainly not to decrease the error caused in direct solving, instead, it tries to transfer this error to a medium variable. At the same time, a parameter is introduced. In order to obtain the best parameter, PSO is used. An algorithm based on this method is presented, and examples show that this algorithm could solve extremely ill-conditioned linear systems correctly and stably.

关键词： particle swarm optimization algorithm auto-parameter ill-conditioned linear system

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Application research of improved K-means algorithm in network intrusion detection

Application research of improved K-means algorithm in networ...

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2017 International Conference on Education and Management Innovation(EMI2017)

作者： Gongrang Zhang Wei Hu School of Management Hefei University of Technology Anhui Transport Interconnection Management Center School of Computer & InformationHefei University of Technology

The original K-means algorithm is sensitive to the selection of the initial clustering center and unstable in the network intrusion detection application based on data mining. In this paper, the optimization ability of particle swarm optimization(PSO) was used to solve the problem that K-means algorithm is sensitive to the selection of the initial clustering center. Different global optimal solutions were obtained by PSO algorithm and used as the basis of choosing the initial clustering center of K-means clustering algorithm. Based on this idea, the K-means algorithm was improved and a network intrusion detection model was established. Experimental results show that the improved K-means clustering algorithm based on PSO has better clustering effect than original K-means clustering algorithm, and can detect more intrusion behaviors in network intrusion detection.

关键词： Network intrusion detection K-means clustering algorithm particle swarm optimization algorithm Initial clustering center

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Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems

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IFAC-PapersOnLine 2016年第3期49卷 347-352页

作者： Aoun, Oussama Sarhani, Malek El Afia, Abdellatif ENSIAS - Mohammed V University Rabat Morocco

The tuning approach consists in finding the most suitable configuration of an algorithm for solving a given problem. Machine learning methods are usually used to automate this process. They may enable to construct robust autonomous artifacts whose behavior becomes increasingly expert. This paper focuses on the restriction of this general problem to the field of air planning and more specifically the crew scheduling problem. Metaheuristics are widely used to solve this problem. Our approach consists of using hidden markov model to find the best configuration of the algorithm based on the estimation of the most likely state. The experiment consists of finding the best parameter values of the particle swarm optimization algorithm for the crew scheduling problem. Our approach has shown that it can be a promising solution for automatic optimization of airline scheduling problems. © 2016

关键词： Problem solving Air transportation Heuristic algorithms Hidden Markov models Learning systems particle swarm optimization (PSO) Scheduling Airline scheduling algorithm for solving Automatic optimization Crew scheduling problems Machine learning methods Meta heuristics Most likely particle swarm optimization algorithm

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