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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Wang, Xinchang Li, Chunqing Tianjin Polytech Univ Coll Comp Sci & Software Tianjin Peoples R China

ISBN: (数字)9781538682463

ISBN: (纸本)9781538682463

Membrane fouling reduces wastewater treatment efficiency and cause financial and energy costs to some extent. The size of membrane flux reflects the degree of membrane pollution. Make timely cleaning membrane or replacement membrane decision to maintain considerable treatment effect on the basis of the membrane flux. particle swarm optimization (PSO) algorithm can quickly find the global optimum. Genetic algorithm (GA) has the property of global convergence. The prediction model used in this paper is based on the PSO-GA hybrid algorithm. The combination of these not only improves the convergence speed of genetic algorithm but also reduces the probability of particle swarm optimization algorithm falling into local optimum. Elman neural network acts as the basic network. Compared with Elman neural network and BP neural network, the prediction accuracy of PSO-GA-Elman is improved.

关键词： MBR particle swarm optimization algorithm Genetic algorithm Prediction Model

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Improved Power Sharing with a Back-to-Back Converter and State-Feedback Control in a Utility-Connected Microgrid 2

Improved Power Sharing with a Back-to-Back Converter and Sta...

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2nd IEEE Texas Power and Energy Conference (TPEC)

作者： Jafari, Hassan Mahmoudi, Maryam Fatehi, Alireza Naderi, Mohammad H. Kaya, Elif Amirkabir Univ Technol Elect Engn Dept Tehran Iran KN Toosi Univ Technol Elect Engn Dept Tehran Iran Texas A&M Univ Elect Engn Dept College Stn TX USA

ISBN: (纸本)9781538610060

This study puts forward a strategy for optimal power sharing control in a microgrid that is connected to a utility grid through a back-to-back (B2B) converter. In grid-connected mode, the B2B converter totally isolates the microgrid from the utility grid in terms of voltage and frequency. In the proposed strategy, a pre-specified amount of power exchanged between the utility grid and the microgrid is regulated via active/reactive control in the B2B converter. This regulation means that distributed generation (DG) units supply the rest of microgrid load demand and track load changes through droop control. In both the voltage source inverter (VSI) of the B2B converter and the DG units, state-feedback control is employed as an inner control loop for tracking the state variable reference signals generated by the corresponding outer loops of the variables. Microgrid stability is essential and highly affected by controller parameters, droop coefficients, and the components of inductor-capacitor-inductor filters. In this regard, control is formulated as an optimization problem, for which particle swarm optimization is used to optimally calculate the parameters of the system and the controllers. Objective functions are derived by minimizing an eigenvalue-based function. The PSCAD simulation results demonstrate the effectiveness of the proposed control strategy.

关键词： Back-to-back converter particle swarm optimization algorithm grid-connected mode angle droop power sharing state-feedback control

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Development of particle swarm optimization algorithm

Development of Particle Swarm Optimization Algorithm

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The 6th International Conference on Computer Science & Education(第六届国际计算机新科技与教育学术会议 ICCSE 2011)

作者： Yu Chen Fan Yang Quan Zou Chen Lin School of Information Science & Technology Xiamen UniversityChina

particle swarm optimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in the future investigations are discussed.

关键词： component swarm intelligence evolutionary computation particle swarm optimization algorithm

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An Intelligent Parameter Selection Method for particle swarm optimization algorithm

An Intelligent Parameter Selection Method for Particle Swarm...

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The Fourth International Joint Conference on Computational Science and optimization(第四届计算科学与优化国际大会 CSO 2011)

作者： Yuntao Dai Ying Li Liqiang Liu College of Science Harbin Engineering University Heilongjiang China College of Automation Harbin Engineering University Heilongjiang China

For the problem of particle swarm optimization parameters selection, a kind of intelligent method to optimum parameters selection using another particle swarm optimization algorithm is proposed. Firstly it analyzes the effect of each parameter on algorithm performance in detail. Then it takes parameter selection of PSO algorithm as a complex optimization problem, sets appropriate fitness function to describe optimization performance, and uses PSO-PARA algorithm to optimize the parameters selection method of PSO-OPT algorithm. Tests to the benchmark function show that these parameters are better than the experience parameters test results in the optimal fitness, the mean value of optimal fitness, convergence rate.

关键词： parameter optimization particle swarm optimization algorithm analysis of parameters

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Developing an optimized artificial intelligence model for S&P 500 option pricing: A hybrid GARCH model

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INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING 2020年第3期7卷

作者： Hajizadeh, Ehsan Amirkabir Univ Technol Tehran Polytech Dept Ind Engn & Management Syst 424 Hafez Ave Tehran *** Iran

In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by particle swarm optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black-Scholes model.

关键词： Option pricing volatility GARCH-type models neural network neuro-fuzzy network particle swarm optimization algorithm

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Ascent Trajectory optimization of Hypersonic Vehicle Based on Improved particle swarm algorithm

Ascent Trajectory Optimization of Hypersonic Vehicle Based o...

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Chinese Automation Congress (CAC)

作者： Wu, Ge Liu, Lei Wang, Yongji Liu, Xing Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China

ISBN: (纸本)9781467371896

By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particle swarm optimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence speed, a PSO strategy is proposed, which consists of the dynamic population structure, opposition-based learning, crossover operator and variable step integral. Combining the improvement of PSO and the optimization strategy, the modified particle swarm optimization ( MPSO) algorithm is formed. The MPSO is applied to optimize the ascent trajectory of hypersonic vehicle. The precision and efficiency of this trajectory optimization method are demonstrated by comparing the results of PSO and MPSO. The simulation results show that the performance of MPSO is significantly superior to PSO either convergence speed or convergent accuracy.

关键词： trajectory optimization particle swarm optimization algorithm variable population opposition-based learning crossover operator

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The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by particle swarm optimization

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淡江理工学刊 2017年第4期20卷 483-490页

作者： Zhixiang Hou Ping Xie Jiqiang Hou

In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.

关键词： Electric Vehicle State-of-charge Estimation Radial Basis Function Neural Network particle swarm optimization algorithm

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Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

Integration of Particle Swarm Optimization and Immune Geneti...

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8th Panhellenic Conference on Artificial Intelligence

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

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Optimal Design for a Retrial Queueing System with State-Dependent Service Rate

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Journal of Systems Science & Complexity 2017年第4期30卷 883-900页

作者： ZHANG Xuelu WANG Jinting MA Qing Department of Mathematics Beijing Jiaotong University School of Science Qilu University of Technology Department of Basic Courses Shandong Women’s University

This paper considers a single server retrial queue in which a state-dependent service policy is adopted to control the service rate. Customers arrive in the system according to a Poisson process and the service times and inter-retrial times are all exponentially distributed. If the number of customers in orbit is equal to or less than a certain threshold, the service rate is set in a low value and it also can be switched to a high value once this number exceeds the threshold. The stationary distribution and two performance measures are obtained through the partial generating functions. It is shown that this state-dependent service policy degenerates into a classic retrial queueing system without control policy under some conditions. In order to achieve the social optimal strategies, a new reward-cost function is established and the global numerical solutions, obtained by Canonical particle swarm optimization algorithm, demonstrate that the managers can get more benefits if applying this state-dependent service policy compared with the classic model.

关键词： particle swarm optimization algorithm retrial queues social optimal strategy statedependent service policy

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SSI effects on seismic behavior of smart base-isolated structures

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GEOMECHANICS AND ENGINEERING 2018年第2期14卷 161-174页

作者： Shourestani, Saeed Soltani, Fazlollah Ghasemi, Mojtaba Etedali, Sadegh Grad Univ Adv Technol Dept Civil & Surveying Engn Kerman Iran Birjand Univ Technol Dept Civil Engn Birjand Iran

The present study investigates the soil-structure interaction (SST) effects on the seismic performance of smart base-isolated structures. The adopted control algorithm for tuning the control force plays a key role in successful implementation of such structures;however, in most studied carried out in the literature, these algorithms are designed without considering the SSI effect. Considering the SSI effects, a linear quadratic regulator (LQR) controller is employed to seismic control of a smart base-isolated structure. A particle swarm optimization (PSO) algorithm is used to tune the gain matrix of the controller in both cases without and with SSI effects. In order to conduct a parametric study, three types of soil, three well-known earthquakes and a vast range of period of the superstructure are considered for assessment the SSI effects on seismic control process of the smart-base isolated structure. The adopted controller is able to make a significant reduction in base displacement. However, any attempt to decrease the maximum base displacement results in slight increasing in superstructure accelerations. The maximum and RMS base displacements of the smart base-isolated structures in the case of considering SSI effects are more than the corresponding responses in the case of ignoring SSI effects. Overall, it is also observed that the maximum and RMS base displacements of the structure are increased by increasing the natural period of the superstructure. Furthermore, it can be concluded that the maximum and RMS superstructure accelerations are significant influenced by the frequency content of earthquake excitations and the natural frequency of the superstructure. The results show that the design of the controller is very influenced by the SSI effects. In addition, the simulation results demonstrate that the ignoring the SSI effect provides an unfavorable control system, which may lead to decline in the seismic performance of the smart-base isolated structure incl

关键词： base isolation soil-structure interaction smart base-isolated structures LQR controller particle swarm optimization algorithm

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