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2nd International Conference on Reliability Systems Engineering (ICRSE)

作者： Wang, Yangyang Han, Minghong Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China

ISBN: (纸本)9781538609187

Complex systems consist of many disciplines or components, which are often difficult to the design optimize as a overall. They need to be broken down into different components, and then coordinate the links between different parts. A TC (Analysis Target Cascade) - one of the multidisciplinary design optimization methods, is an effective way to solve such intricate problems. In the traditional multidisciplinary design optimization methods, there is only one objective function. But the multi-objective optimization problems are often emerged in practical engineering problems. So, we will focus on the multi-objective optimization problems in multidisciplinary design optimization, and solve them with particle swarm optimization. The original problem is firstly decomposed into multiple coupled sub-problems and then coordinate the relation between each sub-problems by ATC method. The system-level sub-problem is a multi-objective optimization problem and the other subsystems are the general single-objective optimization problems, the MOPSO method and the sequence quadratic programming (SQP) method will be used to solve them respectively. The final optimization result is consistent with the optimization result before the original problem is decomposed. Finally, we used two examples to demonstrate the feasibility of particle swarm optimization (PSO) method to get the solution of the multiobjective problems with ATC method.

关键词： multidisciplinary design optimization analysis target cascade multi - objective optimization particle swarm optimization algorithm

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Dynamic equivalent soil characteristics identification using earthquake records

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Earthquake Science 2018年第3期31卷 166-173页

作者： Asskar Janalizadeh Choobbasti Saman Soleimani Kutanaei Department of Civil Engineering Babol University of Technology

Techniques for soil property estimation can be categorized into two main groups, in-situ and laboratory methods. Previous investigations indicated that strong ground motions record provides a very useful tool to estimating the in-situ characteristics of soil. The main objective of the present work is to utilize the particle swarm optimization algorithm（PSOA） integrated with linear site response method to obtain the equivalent soil profile characteristics from the available surface and bedrock earthquake motion records. To demonstrate the numerical efficiency and the validity of this approach, the procedure is validated against an available case. Then this procedure is utilized to identify the soil properties profiles of the site by using strong ground motions data recorded during the Bam earthquake of December 26, 2003. The magnitude and PGA of Bam earthquake were MW 6.6 and 0.8 g respectively.

关键词： identification ground response particle swarm optimization algorithm site effect

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Gridless Net Routing of Integrate Circuit with particle swarm optimization algorithm

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Journal of Energy and Power Engineering 2011年第9期5卷 899-904页

作者： X.C. Huang Power Dispatching ＆ Communication Center Power Grid of Henan Zhengzhou 450000 China

particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is successfully applied in function optimization, neural network design, classification, pattern recognition, signal processing and robot technology and so on. A modified algorithm is presented and applied to the layout of IC design. For a given layout plane, first of all, this algorithm generates the corresponding grid group by barriers and nets＇ ports with the thought ofgridless net routing, establishes initialization fuzzy matrix, then utilizes the global optimization character to find out the best layout route only if it exits. The results of model simulation indicate that PSO algorithm is feasible and efficient in IC layout design.

关键词： particle swarm optimization algorithm gridless net routing layout optimization prufer number.

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Comparative study of optimization intelligent models in wastewater quality prediction

Comparative study of optimization intelligent models in wast...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Yu, Tingting Bai, Yun Chongqing Technol & Business Univ Coll Environm & Resources Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing 400067 Peoples R China

ISBN: (纸本)9781538660577

Sewage treatment process has the following characteristics: nonlinear, delay etc, and is very complicated to establish the model for its control process. A reasonable model is set up for elaborate prediction effluent quality, which can satisfy the standard of the effluent water and requirements of energy saving simultaneously. Extreme learning machine (ELM), i.e the machine learning method that new lately developed has high accuracy, reliability and outstanding performance in prediction To get higher prediction effect, in this paper, there are two ways are proposed to improve the ELM, (1) Optimizing the parameters. The ELM whose input weights and bias threshold are optimized by particle swarm optimization algorithm (PSO) and genetic algorithm (GA), respectively;(2) Changing learning mode. To develop an online sequential learning algorithm (OS) for the ELM with additive or radial basis function (RBF) hidden nodes in a unified framework. Therefore, the several comparison approaches refer to optimize the ELM, e.g., PSO-ELM, GA-ELM, OS-ELM are applied to effluent quality prediction, and chemical oxygen demand (COD) is taken as examples in this paper. The results show that PSO-ELM model has remarkably superior performance on effluent quality prediction than peer models in terms of mean absolute error, mean absolute percentage error, root mean square error, and coefficient of determination

关键词： extreme machine learning particle swarm optimization algorithm genetic algorithm online sequential learning algorithm effluent quality prediction

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Selection of kernel function for least squares support vector machines in downburst wind speed forecasting 11

Selection of kernel function for least squares support vecto...

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11th International Symposium on Computational Intelligence and Design (ISCID)

作者： Li, Zhou Li, Chunxiang Shanghai Univ Dept Civil Engn Shanghai Peoples R China

ISBN: (纸本)9781538685273

In recent years, kernel machine learning algorithm (e.g. least square support vector machine, LSSVM) is a commonly method for time series prediction, as the main unit, the type of kernel function is very crucial. In this paper, a hybrid model of variational mode decomposition (VMD), particle swarm optimization (PSO) and LSSVM is proposed to forecast the downburst wind speed series. Linear kernel function, polynomial kernel function, radial basis function, Morlet wavelet kernel function, Mexican Hat wavelet kernel function and their combinations are selected to demonstrate the influence of different kernel functions on prediction results. The results indicate that the Morlet+RBF combined kernel function is considerably effective in enhancing the forecasting accuracy of the VMD-PSO-LSSVM model.

关键词： kernel functions least squares support vector machine particle swarm optimization algorithm variational mode decomposition wind speed forecasting

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Iterative PSO algorithms for GRAP Problems

Iterative PSO Algorithms for GRAP Problems

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2019 International Conference on Artificial Intelligence, Control and Automation Engineering(AICAE 2019)

作者： Fang LU Dong-kui LI Faculty of Mathematics Science School Baotou Teachers College Editorial Department of Journal Baotou Teachers College

This paper studies the grap problems of two-state, in which the subsystem allows components to be mixed(i.e. the subsystem selects components from several types of heterogeneous components, and the number of selected components types >=1). Each component has a fixed reliability, weight and price, and determines the number of selected components, so that the system has the greatest reliability under the given cost and weight constraints. The coding method of the solution is that the number of elements of each type of subsystem is a variable, and the whole system is arranged in the order of subsystems to form row vectors. An iterative particle swarm optimization algorithm with fixed compression coefficient and dynamic inertia weight is constructed to solve the problem. Typical improved fyffe problems are tested, and the optimal solutions are obtained, which are consistent with the results given by the substitution constraint method. The pso algorithm presented in this paper can effectively solve the grap problem which is allowed to mix components in subsystems.

关键词： Mixed components The reliability redundancy allocation problem particle swarm optimization algorithm Encoding Optimal solution

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Short-Term Load Forecasting Using Optimized LSTM Networks Based on EMD 10

Short-Term Load Forecasting Using Optimized LSTM Networks Ba...

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10th International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Li, Tiantian Wang, Bo Zhou, Min Zhang, Lianming Zhao, Xin Nanjing Univ Sch Management & Engn Nanjing Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538693896

Short-term load forecasting is one of the crucial sections in smart grid. Precise forecasting enables system operators to make reliable unit commitment and power dispatching decisions. With the advent of big data, a number of artificial intelligence techniques have been used to predict the load of the next day. Nevertheless, due to the noise of raw data and the randomness of power load, forecasting errors of existing approaches are relatively large. In this study, a short-term load forecasting method is proposed on the basis of empirical mode decomposition and long short-term memory networks, the parameters of which are optimized by a particle swarm optimization algorithm. Essentially, empirical mode decomposition can decompose the original time series of historical data into relatively stationary components and long short-term memory network is able to emphasize as well as model the timing of data, the joint use of which is expected to effectively apply the characteristics of data itself, so as to improve the predictive accuracy. The effectiveness of this research is exemplified on a realistic data set, the experimental results of which show that the proposed method has higher forecasting accuracy and applicability, as compared with existing methods.

关键词： Short-term load forecasting long short-term memory empirical mode decomposition particle swarm optimization algorithm

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Multi-Objective optimization of Difference Coefficient of Generator Excitation System during Multi-Period

Multi-Objective Optimization of Difference Coefficient of Ge...

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China International Conference on Electricity Distribution (CICED)

作者： Wang Ping Fan Lei Liang Ling Chen Feng Wang Guosong Wan Huijiang Zhou Siming Guizhou Power Grid Co Ltd Guiyang 550002 Guizhou Peoples R China NR Elect Co Ltd Nanjing 211102 Jiangsu Peoples R China

ISBN: (纸本)9781538667750

This paper proposes a multi-objective optimization model for the difference coefficient of generator excitation system to improve grid operation voltage level during multi-period and reduce grid loss. The minimum voltage fluctuation at power grid pilot point during multi-period and the minimized active grid loss are taken as the objective functions, while the power flow equations are used as the constraints. The particle swarm optimization (PSO) algorithm is employed to solve the optimization model. The effects of the difference coefficients of generator excitation system on power grid operation voltage are analyzed in detail. The simulation results indicate that the operating voltage level of power grid can be effectively improved and the grid loss can be significantly reduced.

关键词： difference coefficient generator excitation system particle swarm optimization algorithm power grid reactive power and voltage

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A Optimal Path Planning of Multi-destinations for Mobile Robot in Complex Environment

A Optimal Path Planning of Multi-destinations for Mobile Rob...

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Chinese Automation Congress (CAC)

作者： Liu, Ping Wang, Chunying Qin, Hongzheng Sun, Jinju Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9781728113128

A lot of progress has been made in the research of point-to-point and complete coverage path planning of mobile robots, while the multi-destinations path planning is seldom reported in the literatures. Based on particle swarm optimization and vortex search algorithms, this paper proposes a multi-destinations path planning approach, which is suited to plan a feasible, safe and optimal path between multi-destinations in complex home environment for mobile robot. Firstly, the sequence of the destinations is quickly optimized by using the particle swarm optimization algorithm. Then, the collision-free path between destinations is obtained via vortex search algorithm with its advantages of high efficiency and small computation. Finally, simulation results show that the proposed multi-destinations path planning approach has the good explorative and exploitation ability, while the path planned by proposed approach is smooth, short.

关键词： Multi-destinations optimization particle swarm optimization algorithm Vortex search algorithm Optimal path planning

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particle swarm and bat-inspired optimization algorithm-based parameter identification of Krasnosel’skii-Pokrovskii model for piezoelectric hysteresis

Particle swarm and bat-inspired optimization algorithm-based...

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第五届国际仿生工程学术大会(ICBE2016)

作者： Rui Xu Miaolei Zhou Yantao Tian Department of control science and engineering Jilin University

The piezoelectric ceramics is a kind of novel intelligent materials and possesses advantages of ultrafine resolution, high stiffness and fast frequency response. It is an excellent choice as an actuator to be applied widely to micro-nano positioning system. However, one of the drawbacks of the piezoelectric actuator is the obvious hysteresis nonlinearity to limit the accuracy of positioning. The present study focuses on describing the hysteresis nonlinearity between input voltage and output displacement of piezoelectric ceramics. In order to investigate the hysteresis nonlinearity of piezoelectric ceramics, a series of models are presented, such as BoucWen model, Preisach model, Krasnosel’skii-Pokrovskii model and so on. However, the main difficulty is how to select an effective method to identify the unknown parameters of these hysteresis nonlinearity models. In this paper, we propose a novel hybrid optimization algorithm of particle swarm and bat-inspired to identify the density function of Krasnosel’skii-Pokrovskii model. The proposed hybrid optimization algorithm has the fast convergence of bat-inspired and the global search ability of particle swarm. During operation of the optimization algorithm, we divided the swarm into two different parts randomly as the initial value. One set of data is optimized by particle swarm algorithm and find the optimal value i.e. best1, the other set of data is optimized by bat-inspired algorithm and the optimal value is best2. Then we select the bigger one between best1 and best2 i.e. Gbest as the global optimal value of the two algorithms for the next iteration *** taking advantage of the optimization algorithm, an objective function is defined firstly. Then the density function of Krasnosel’skii-Pokrovskii model is identified by using bat-inspired algorithm and hybrid optimization algorithm of particle swarm and bat-inspired respectively. The simulations show that the displacement error of identification of Kras

关键词： particle swarm optimization algorithm bat-inspired algorithm Krasnosel’skii-Pokrovskii model

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