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Study on the Bike-sharing Inventory Rebalancing and Vehicle Routing for Bike-sharing System

Transportation Research Procedia 2019年 39卷 624-633页

作者： Lei Shi Yong Zhang Weina Rui Xinzheng Yang School of Transportation Southeast University Southeast university jiulonghu campus Nanjing and 211100 China Transportation Bureau of Huishan District Huishan District Xizhang West Road No.100 Wuxi and 214000 China School of Automobile Chang’an University Middle Section of South 2nd Ring Road Xi’an and 710064 China

Bike-sharing system has been launched in many cities, due to the essential merits. Along with the convenience brought by the rapid development of bike-sharing system, several severe problems also arise. The most serious problem is the uneven distribution of bicycles. Thus the VRP model for bike-sharing inventory rebalancing and vehicle routing is formulated. Additionally, an improved particle swarm optimization (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Five maintenance trucks are designated to execute all delivery tasks required by 25 spots. Capacities of all of the maintenance trucks are almost fully utilized. It is of considerable significance for bike-sharing enterprises to make optimal bike schedule.

关键词： Bike-sharing system inventory rebalancing vehicle routing problem particle swarm optimization algorithm

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particle swarm optimization Approach for the Segmentation of Retinal Vessels from Fundus Images 14th

Particle Swarm Optimization Approach for the Segmentation of...

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14th International Conference on Image Analysis and Recognition (ICIAR)

作者： Khomri, Bilal Christodoulidis, Argyrios Djerou, Leila Babahenini, Mohamed Chaouki Cheriet, Farida Polytech Montreal Lab LIV4D Montreal PQ Canada Univ Biskra LESIA Lab Biskra Algeria

ISBN: (纸本)9783319598765;9783319598758

In this paper, we propose to use the particle swarm optimization (PSO) algorithm to improve the Multi-Scale Line Detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied to find the best arrangement of scales in the basic line detector method. The segmentation performance was validated using a public high-resolution fundus images database containing healthy subjects. The optimized MSLD method demonstrates fast convergence to the optimal solution reducing the execution time by approximately 35%. For the same level of specificity, the proposed approach improves the sensitivity rate by 3.1% compared to the original MSLD method. The proposed method will allow to reduce the amount of missing vessels segments that might lead to false positives of red lesions detection in CAD systems used for diabetic retinopathy diagnosis.

关键词： Retinal blood vessel segmentation optimization particle swarm optimization algorithm Multi-scale Line Detection

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Optimal Plan for Step-Stress Accelerated Life Test with Two Stress Variables for Lognormal Data

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE 2018年第A4期42卷 2259-2271页

作者： Hakamipour, Nooshin Buein Zahra Tech Univ Dept Math Buein Zahra Qazvin Iran

A simple step-stress accelerated life testing plan with two stress variables is considered, when the failure times in each level of stress follow the lognormal distribution. The lognormal distribution is commonly used to model certain types of data that arise in several fields of engineering such as, for example, different types of lifetime data or coefficients of wear and friction. The problem of choosing the optimal times to change the stress level is investigated by minimizing the asymptotic variance of the reliability estimate and maximizing the determinant of Fisher information matrix. In this paper, we obtain the optimal bivariate step-stress accelerated life test using both the criteria. Due to the nonlinearity and complexity of problem, the particle swarm optimization algorithm is developed to calculate the optimal hold times. In this method, the research speed is very fast and the optimization ability is more. To illustrate the effect of the initial estimates on the optimal values, sensitivity analysis is performed. Finally, numerical studies are discussed to illustrate the proposed criterion. Simulation results show that the proposed optimum plan is robust.

关键词： Bivariate step-stress accelerated life test Determinant optimality Optimal design particle swarm optimization algorithm Reliability

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Research on Multi-Objective Multidisciplinary Design optimization Based on particle swarm optimization 2

Research on Multi-Objective Multidisciplinary Design Optimiz...

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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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