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2018 International Conference on Mechanical,Electrical,Electronic Engineering & Science(MEEES 2018)

作者： Xiangjie Chen School of Mathematics Southeast University

ISBN: (纸本)9781510868755

The optimal sizing design of truss structures is studied using the recently proposed particle swarm optimization algorithm(PSOA), in which the social behavior of birds is mimicked. Individual birds in a flock exchange information about their position, velocity and fitness, and the behavior of the flock is then influenced to increase the probability of migration to regions of high fitness. A simple approach is presented to accommodate the stress and displacement constraints during initial stages of the swarm searches. In this approach, increased social pressure, at the cost of cognitive learning, is exerted on infeasible birds to increase their rate of migration to feasible regions. Numerical results are presented for a number of well-known test functions, with dimensionality of up to 21.

关键词： Truss structure modal sensor particle swarm optimization algorithm

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Multi-label classification algorithm research based on swarm intelligence

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2016年第4期19卷 2075-2085页

作者： Wu, Qinghua Liu, Hanmin Yan, Xuesong Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Hubei Peoples R China Wuhan Inst Ship Bldg Technol Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

Since data and resources have massive feature and feature of data are increasingly complex, traditional data structures are not suitable for current data anymore. Therefore, traditional single-label learning method cannot meet the requirements of technology development and the importance of multi-label leaning method becomes more and more highlighted. K-Nearest Neighbor (KNN) classification method is a lazy learning method in data classification methods. It does not need data training process and theoretical system is mature. In addition, principle and implementation is simple. This paper proposed improvements strategies only considers numerical feature of sample KNN when classifying, but not consider the disadvantage of sample structure feature. This paper introduced particle swarm optimization algorithm into KNN classification and make adjustments to Euclidean distance formula in traditional KNN classification algorithm and add weight value to each feature. Using adjusted distance formula to train training data through particle swarm optimization algorithm and optimized a set of weight value for all features and put these optimized weight values to adjusted distance formula and calculated the distance between each example in test data set and in training data set and predict the test data set. Experiment results show that weighted KNN classification algorithm based on particle swarm optimization algorithm can achieve better classification accuracy than traditional KNN classification algorithm.

关键词： Multi-label K-Nearest Neighbor particle swarm optimization algorithm Weighted KNN classification algorithm

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Multi-periods distribution feeder reconfiguration at the presence of distributed generation through economic assessment using a new modified PSO algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016年第1期31卷 321-331页

作者： Nayeripour, Majid Mahboubi-Moghaddam, Esmaeil Khooban, Mohammad Hassan Shiraz Univ Technol Dept Elect Engn Shiraz Iran

Over the past few years, distribution system operators try their best in order to obtain the well-balanced distribution systems to reduce the power loss, decrease the operation cost and improve the reliability indices. This paper presents an efficient method to solve the multi-periods distribution feeder reconfiguration (DFR) with respect to the presence of Distributed Generators (DGs). Most studies so far have investigated reconfiguration problem as a single period problem considering a fixed level of load. However, in this study, time-varying characteristics of load profiles and line failure rates are considered. The proposed framework formulates and studies the direct and implied costs of power supply, reliability, energy loss, and switching operations, simultaneously. By considering these conditions to the DFR problem, the number of decision variables is significantly increased and the problem becomes more complicated than before. To this end a new modified particle swarm optimization (PSO) algorithm, compatible with the multi-periods problems, is presented. In the proposed algorithm, the costs of individual periods and the total cost are considered simultaneously in order to update the particles. To evaluate the performance of the proposed method, the results are compared with the original one. A typical distribution test system is used to demonstrate the performance of the proposed approach.

关键词： Distribution feeder reconfiguration distributed generation reliability particle swarm optimization algorithm

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Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid optimization algorithm

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algorithmS 2017年第1期10卷 19-19页

作者： Wang, Ru Tan, Chao Xu, Jing Wang, Zhongbin Jin, Jingfei Man, Yiqiao China Univ Min & Technol Sch Mechatron Engn 1 Daxue Rd Xuzhou 221116 Peoples R China CRRC Tangshan Corp Ltd 3 Changqian Rd Fengrun Dist 063000 Tangshan Peoples R China

In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.

关键词： self-tuning PID controller genetic algorithm particle swarm optimization algorithm pressure control

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Application of Two Modified Autonomous Development algorithms in Robot Obstacle Avoidance

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电脑学刊 2017年第3期28卷 235-250页

作者： Hong-Ge Ren Rui Yin Tao Shi Fu-Jin Li

In view of high dimension, the difficulty of training, the problem of slow learning speed in the application of BP neural network in mobile robot path planning, an algorithm of reinforcement Q learning based on online sequential extreme learning machine (Q-OSELM algorithm) was proposed in this paper. And then, due to the random selection of weight and threshold parameters, it also proposes an extreme learning machine algorithm optimized by particle swarm (PSO-ELM algorithm) in this paper. Firstly, Q-OSELM algorithm obtains current environment and the status information of the robot through the characteristic of reinforcement learning, which combines dynamic network with supervised learning. After that, the online sequential extreme learning machine is used to approximate the function of the current status to get the rewards and punishments of the current status; Secondly, it is used to solve the problem of slow training speed by the characteristic of less parameter settings and better generalization performance. PSO-ELM algorithm is used to optimize the input weights and the hidden layer bias of the extreme learning machine which have been seen as the particle of particle swarm optimization algorithm to improve the network structure of the extreme learning machine. It could overcome inaccuracy of traditional extreme learning machine through particle swarm optimization algorithm. Finally, the performance of two learning algorithms is verified. The simulation experimental results show that the Q-OSELM learning algorithm improves the initiative of machine learning. And compared with the Q-OSELM algorithm, the PSO-ELM algorithm has better generalization ability and higher training precision. Simulation experiments are carried out to verify the stability and convergence of the two algorithms.

关键词： autonomous learning obstacle avoidance online sequential extreme learning machine particle swarm optimization algorithm reinforcement Q learning

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Research on inverse problems of heat flux and simulation of transient temperature field in high-speed milling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第9-12期84卷 2067-2078页

作者： Wei, Bo Tan, Guangyu Yin, Ningxia Gao, Linlin Li, Guanghui Guangdong Ocean Univ Zhanjiang Guangdong Peoples R China

Cutting temperature is an important factor which directly affects cutting tool wear, cutting tool life, machined surface quality, and accuracy in the high-speed machining (HSM) process. It is very important to study the distribution law of cutting temperature for the HSM process. In this paper, the self-developed embedded temperature measuring tool holder system (ETMTHS) is employed to measure the continuous temperature of carbide end milling tool tip. The dynamic temperature field model of solid cemented carbide milling cutter is established by using heat source method, and the heat flux in power series form is solved by using the particle swarm optimization (PSO) algorithm. At the same time, an optimization algorithm to solve the inverse problem of heat conduction is given. The solution of heat flux is converted into the solution of the optimal value problem. Using optimization algorithm, the inverse heat conduction problem can be solved successfully. The temperature and its gradient distribution of solid cemented carbide milling cutter are obtained by analyzing the continuous milling temperature of ANSYS simulation. The comparison results show a good agreement between the simulation temperature and measuring temperature.

关键词： Solid cemented carbide milling cutter Dynamic temperature field model particle swarm optimization algorithm Heat flux Inverse heat conduction problem

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Application of mixed kernels function (MKF) based support vector regression model (SVR) for CO₂ - Reservoir oil minimum miscibility pressure prediction

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FUEL 2016年第0期184卷 590-603页

作者： Zhong, Zhi Carr, Timothy R. West Virginia Univ Dept Geol & Geog Morgantown WV 26506 USA

Carbon dioxide (CO2) injection into oil reservoirs is considered a mature enhanced oil recovery (EOR) technique for conventional reservoirs. The local displacement efficiency of the CO2-EOR process is highly dependent on the minimum miscibility pressure (MMP), estimating this parameter is critical to design of the CO2 injection process. Traditional empirical methods to test the CO2-oil MMP are time consuming and expensive;derived correlations are fast but not accurate. Therefore, an efficient and reliable method to determine MMP is beneficial. In this study, a mixed kernels function (MKF) based support vector regression (SVR) model was developed and used to predict the MMP for both pure and impure CO2 injection cases. Four parameters were chosen as input parameters: (1) reservoir temperature;(2) average critical temperature;(3) molecular weight of pentane plus (C5+) fraction of crude oil, and;(4) the ratio of volatile components to intermediate components in crude oil. MMP was selected as the desired output parameter to train and test this newly developed model. The performance of basic kernels function based SVR model is compared with that of this newly developed MKF-SVR model. The well-trained MFK-SVR was compared with three well-established published correlations, demonstrated the highest correlation coefficient (R of 0.9381), lowest root mean square error (RMSE of 1.9151), smallest average absolute error (AAE of 1.1406) and maximum absolute error (MAE of 4.6291). We believe that the proposed MFK-SVM model is a more reliable and stable regression model to predict MMP. In addition, a sensitivity analysis was conducted to evaluate the physical correctness. It indicates that the predicted results from the newly developed model are in excellent agreement with previous empirical work. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： CO2-oil minimum miscibility pressure CO2 enhanced oil recovery Support vector regression Mixed kernels function particle swarm optimization algorithm

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A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期84卷 371-379页

作者： Zheng, Hao Feng, Yixiong Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China

Cloud manufacturing (CMfg) is a kind of advanced service-oriented manufacturing model with on-demand use of various lifecycle-resources. Resource service selection (RSS) is one of the critical techniques for implementing CMfg, which is applied for building flexible and loosely coupled service application to requestors. With lots of resource services owning similar functionality in RSS, quality of service (QoS) which can reflect user experience of service is often considered as a key technology to distinguish resource services for RSS. However, because of the heterogeneous QoS values, vast amounts of homogeneous resources and dynamic customer requirements in CMfg, the issue of how to measure fuzzy QoS and select the best services considering design preference, are rarely studied in CMfg. In this paper, we propose an integrated resource service selection approach to assist requesters to obtain optimal manufacturing services. Firstly, the problem description on resource service selection in CMfg is summarized. Then, a design preference-based QoS description model of CMfg is proposed and a QoS computation model based on fuzzy theory is presented for QoS measurement. Based on the above model, particle swarm optimization (PSO) algorithm is adopted to select the optimal service composition. Finally, a numerical example is given to validate the effectiveness and efficiency of the proposed approach.

关键词： Cloud manufacturing Quality of service Service composition particle swarm optimization algorithm

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A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2016年第16期230卷 2792-2805页

作者： Si, Lei Wang, Zhongbin Liu, Xinhua China Univ Min & Technol Sch Mechatron Engn Daxue Rd 1 Xuzhou 221116 Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

关键词： Shearer running status rough sets wavelet neural network particle swarm optimization algorithm

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考虑车辆限速和风险公平性的危险品运输网络双目标优化模型

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管理科学 2019年第5期

作者：王伟张宏刚丁黎黎张文思

在危险品运输网络中，对不同路段上危险品运输车辆的最高车速设置不同的限制值，能在很大程度上影响危险品运输商的路径选择，从而导致不同的危险品运输网络总风险和总成本。本文首先基于车辆限速的方法，构建了考虑风险公平性的危险品... 详细信息

在危险品运输网络中，对不同路段上危险品运输车辆的最高车速设置不同的限制值，能在很大程度上影响危险品运输商的路径选择，从而导致不同的危险品运输网络总风险和总成本。本文首先基于车辆限速的方法，构建了考虑风险公平性的危险品运输网络优化的双层规划模型，上层规划以网络总风险和总时间成本的最小化为目标，并通过增加路段容量和区域风险约束以确保风险分布的公平性，下层规划以危险品运输企业的总时间成本最小化为目标；然后，采用粒子群优化算法求解了该模型；最后，通过两个算例验证了模型和算法的有效性。计算结果表明，政府部门运用车辆限速的方法，并且考虑风险平等性约束，在兼顾运输企业效益的同时，能有效地降低危险品运输网络的总风险和保证区域风险分布的公平性。

关键词：公路运输车辆限速粒子群优化算法危险品运输风险公平性双层规划　　Highway Transportation Vehicle Speed Limit particle swarm optimization algorithm Risk Equity Bilevel Programming

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