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3rd European Network Intelligence Conference (ENIC)

作者： Morzy, Mikolaj Kruk, Tomasz Wroclaw Univ Sci & Technol Inst Informat Wybrzek Wyspianskiego 25 PL-50370 Wroclaw Poland Poznan Univ Tech Inst Comp Sci Piotrowo 2 PL-60965 Poznan Poland

ISBN: (纸本)9781509034550

Online communities play an important role in the world of contemporary social networks. Due to the ubiquitous availability of ICT tools individuals can easily form small, tight, dense groups around various topics and subjects of interest. These online communities share many similarities with general social networks, but they display some interesting distinguishing characteristics. In particular, online communities are usually subject-centered, require some degree of supervision and maintenance in order to continue operations, and provide higher quality user-generated content as compared with larger social networks. Many different approaches have been proposed in the past regarding methods of online community analysis and modeling. Agent-based methods have been successfully employed to simulate online community formation and evolution. However, simulation-based methods inherently suffer from the arbitrary choice of parameters that may produce radically different online community structures as the result of minuscule changes of input parameters. In this paper we examine the usability of a particular agent-based method, namely, the particle swarm model, for online community analysis. We propose a simple model that uses a swarm of particles paradigm to simulate the behavior of users and the formation of online communities and we measure the influence that changing of the swarm parameters has on the resulting online community structure.

关键词： particle swarm algorithm community analysis social networks

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Human exercise physiology index evaluation method based on a BP neural network

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 2112-2116页

作者： Su, Guolin Ningxia Normal Coll Ningxia 756000 Peoples R China

A BP neural network is proposed in this study to measure the physiology index after exercise and to evaluate the human exercise physiology index. Based on the analysis of the traditional BP algorithm, the improved BP algorithm combined with particle swarm algorithm is proposed to improve the BP neural network algorithm. This algorithm is applied to college students' human exercise physiology index through computer simulation and actual extended human exercises. Results show that the accuracy of the improved BP neural network algorithm is high and that the sample requirement is relatively reasonable. The algorithm is highly recognized by the industry.

关键词： Human exercise physiology index evaluation BP algorithm particle swarm algorithm

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Application of support vector machine and particle swarm optimization in Micro Near Infrared Spectrometer 8

Application of support vector machine and particle swarm opt...

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8th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT) - Design, Manufacturing, and Testing of Micro- and Nano-Optical Devices and Systems;and Smart Structures and Materials

作者： Xiong, Yuhong Liu, Yunxiang Shu, Minglei Shanghai Inst Technol Coll Comp Sci & Technol Shanghai 201418 Peoples R China

ISBN: (纸本)9781628419207

In the process of actual measurement and analysis of micro near infrared spectrometer, genetic algorithm is used to select the wavelengths and then partial least square method is used for modeling and analyzing. Because genetic algorithm has the disadvantages of slow convergence and difficult parameter setting, and partial least square method in dealing with nonlinear data is far from being satisfactory, the practical application effect of partial least square method based on genetic algorithm is severely affected negatively. The paper introduces the fundamental principles of particle swarm optimization and support vector machine, and proposes a support vector machine method based on particle swarm optimization. The method can overcome the disadvantage of partial least squares method based on genetic algorithm to a certain extent. Finally, the method is tested by an example, and the results show that the method is effective.

关键词： Micro near infrared spectrometer spectral analysis support vector machine particle swarm algorithm

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Vehicle Scheduling base on the Improved particle swarm

Vehicle Scheduling base on the Improved Particle Swarm

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World Automation Congress (WAC)

作者： Zhang, Xueying Zheng, Xianghan Ye, Huijuan Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China

ISBN: (纸本)9781889335513

In order to effectively prevent particle swarm into local optimum and enhance the ability of particles to find the optimal solution, the paper proposes an improved particle swarm algorithm. The paper is on the background of vehicle scheduling, which is using the load priority strategy, and establishes the model for vehicle scheduling problem with load and vehicle scheduling problem with soft time windows. The experimental results show that the improved particle swarm can quickly and efficiently obtain the optimal solution for vehicle routing problem.

关键词： Vehicle Routing Problem particle swarm algorithm Load Priority Strategy

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Multi-objective reactive power optimisation approach for the isolated grid of new energy clusters connected to VSC-HVDC

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The Journal of Engineering 2017年第13期2017卷 1024-1028页

作者： Shengli Yin Linlin Wu Wei Song Xiao Wang North China Electric Power University China Lianchi District Baoding 071003 People's Republic of China Research Institute State Grid Jibei Electric Power Co. Ltd Xicheng District Beijing 100045 People's Republic of China

It is known that reactive power optimisation is of great significance to improve the electrical power quality. How the security, reliability, and economy of the system can be effectively improved by reactive power optimisation has attracted the attention of scholars. In this study, according to the characteristics of an isolated grid of the new energy clusters connected to voltage source converter-based high-voltage direct current, the multi-objective reactive power optimisation model, which takes the network loss, and the reactive power margin of new energy clusters into consideration, is established. In particular, the maximisation of reactive power margin of new energy clusters is chosen as one goal, thus the response speed of voltage control can be effectively improved, and the preventive control of transient voltage stability of new energy clusters will be realised. Afterwards, using the weighted method the multi-objective optimisation model can be transformed into a single-objective optimisation model and the particle swarm algorithm is utilised to obtain the optimal solution. Finally, the simulation results show that the reactive power optimisation approach proposed can effectively reduce the network loss of the system, increase the reactive power margin of the new energy clusters, and improve the quality of the system voltage.

关键词： reactive power control HVDC power convertors voltage control power grids power supply quality particle swarm optimisation power system stability power system transients energy clusters multiobjective optimisation model single-objective optimisation model reactive power margin multiobjective reactive power optimisation approach isolated grid electrical power quality multiobjective reactive power optimisation model VSC-HVDC voltage control transient voltage stability particle swarm algorithm

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STPSO: Optimal Configuration for Cloud Environments

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China Communications 2016年第10期13卷 198-208页

作者： Hongxia Zhang Fei Wang Yang Zhang Jiuyun Xu School of Computer and Communication China University of Petroleum School of Economics & Management China University of Petroleum

With the increasing number of resources provided by cloud environments, identifying which types of resources should be rent when deploying an application is often a difficult and error-prone process. Currently, most cloud environments offer a wide range of configurable resources, which can be combined in many different ways. Finding an appropriate configuration under cost constraints while meeting requirements is still a challenge. In this paper, software product line engineering is introduced to describe cloud environments, and configurable resources are abstracted as features with attributes. Then, a Self-Tuning particle swarm Optimization approach(called STPSO) is proposed to configure the cloud environment. STPSO can automatically adjust the arbitrary configuration to a valid configuration. To evaluate the performance of the proposed approach, we conduct a series of comprehensive experiments. The empirical experiment shows that our approach reduces time and provides a reliable way to find a correct and suitable cloud configuration when dealing with a significant number of resources.

关键词： cloud environment resource configuration software product line particle swarm algorithm

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A toll-based bi-level programming approach to managing hazardous materials shipments over an intermodal transportation network

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2016年 47卷 208-221页

作者： Assadipour, Ghazal Ke, Ginger Y. Verma, Manish Mem Univ Newfoundland Fac Business Adm St John NF A1B 3X5 Canada McMaster Univ DeGroote Sch Business Hamilton ON L8S 4L8 Canada

This research proposes a bi-level bi-objective model to regulate the usage of rail intermodal terminals for hazardous materials (hazmat) shipments, where government imposes tolls to deter carriers from using certain terminals. The complexity of the resulting mathematical program motivates the development of a hybrid speed-constrained multi-objective particle swarm optimization algorithm, which is then integrated with CPLEX, to solve the model. Through a real problem instance based on the intermodal service chain of Norfolk Southern in US, the toll-setting model is examined and further compared with a regular network design approach, in which certain terminals are closed to hazmat containers. The computational results show that the toll-setting policy is more practical and efficient, and the two models can be combined as a two-stage strategy in long-term hazmat transportation regulations. Additional managerial insights are derived for different stakeholders. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Bi-level programming Facility location Rail-truck intermodal transportation Hazardous materials particle swarm algorithm

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Determining the Optimum Cross-sectional Area of the Medium Voltage Feeder for Loss Reduction and Voltage Profile Improvement based on particle swarm algorithm

Determining the Optimum Cross-sectional Area of the Medium V...

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International Conference on Environment and Electrical Engineering

作者： F. Hashemi A. R. Alizade S. J. Zebardast N. Ghadimi Islamic Azad University Science And Research branch-Tehran Ardabil Province Electricity Distribution co Khorramshahr Combined Cycle Power Plant Islamic Azad University

ISBN: (纸本)9781424487790

In this paper, the optimal cross-sectional area of medium voltage feeder will be designed based on particle swarm optimization (PSO) so as to reduce power and energy losses and improve voltage profile in distribution system. To analyze the network status in any step, DC power flow is utilized which is done robustly and with high convergence rate of the system. In the proposed method, medium voltage feeders' cross-sectional areas are calculated regarding not only economic factors and power loss costs, but also constraints such as current carrying capacity of feeder and maximum permissible voltage drop. Since particle swarm algorithm is used as the optimization tool, with high possibility, obtained solution would be near the absolute optimum point. This method was implemented in MATLAB7 environment. Numerical simulation results and comparison with different references, shows the proposed method capability in desired problem solving.

关键词： Determining cross-sectional area of the medium voltage feeder particle swarm algorithm Loss reduction and voltage profile improvement

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A Two-loop Optimization Strategy for Multi-objective Optimal Experimental Design

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IFAC-PapersOnLine 2016年第7期49卷 803-808页

作者： Yu, Hui Yue, Hong Halling, Peter Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1XW United Kingdom Department of Pure and Applied Chemistry University of Strathclyde Glasgow G1 1XL United Kingdom

A new strategy of optimal experimental design (OED) is proposed for a kinetically controlled synthesis system by considering both the observation design and the input design. The observation design that combines sampling scheduling and measurement set selection is treated as a single optimization problem arranged in the inner loop, while the optimization of the input intensities is conducted in the outer loop. This multi-objective dynamic optimization problem is solved via the integration of the particle swarm algorithm (for the outer loop) and the interior-point method (for the inner loop). Numerical studies demonstrate the efficiency of this optimization strategy and show the effectiveness of this integrated OED in reducing parameter estimation uncertainties. In addition, process optimization of the case study enzyme reaction system is investigated with the aim to obtain the maximum production rate by taking into account of the experimental cost. © 2016

关键词： Multiobjective optimization Enzymes Optimization Parameter estimation Process control Statistics Dynamic optimization problem (DOP) Enzyme reaction Estimation uncertainties Input design Maximum production rate observation strategy Optimal experimental designs particle swarm algorithm

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Pulse shape analysis for γ-ray tracking (Part II): Fully informed particle swarm algorithm applied to AGATA

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EUROPEAN PHYSICAL JOURNAL A 2011年第10期47卷 131-131页

作者： Schlarb, M. Gernhaeuser, R. Klupp, S. Kruecken, R. Tech Univ Munich Phys Dept E12 D-85748 Garching Germany TRIUMF Vancouver BC V6T 2A3 Canada

AGATA is an array of highly segmented HPGe detectors using the technique of gamma-ray tracking to reconstruct the scattering path of gamma-rays interacting within the active material. A basic requirement therefore is a precise reconstruction of the individual interaction locations within the detectors. This is possible through the use of pulse shape analysis (PSA) which, however, has to be conducted in real time due to the high data rates expected. We report on the use of a fully informed particle swarm (FIPS) algorithm for PSA. The achieved position resolution, in the order of a few millimeters, is within the requirements of a.-ray tracking array. The energy resolutions in Doppler-corrected spectra of a simulated in-beam experiment are comparable to those of a non-real-time extensive PSA based on a full grid search.

关键词： tracking arrays Energy resolution particle swarm algorithm ray tracking shape analysis Pulse shape high purity Ge detectors Real time Data transfer rates

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