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16th International Symposium on Communications and Information Technologies (ISCIT)

作者： Li, Wenjing Yu, Peng Yin, Mengjun Feng, Lei Qiu, Xuesong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781509040995

To solve abnormal coverage problems in cellular networks caused by some internal or external failures without power control, an automated Cell Outage Compensation (COC) scheme based on antenna downtilt adjustment of Base Stations (BSs) is proposed. We firstly give the self-organized processes for COC, then use an algorithm named Autonomic Downtilt Adjustment particle swarm algorithm (ADAPSA) to resolve it. In the algorithm, we construct coverage compensation as a multi-objective non-linear optimization problem, and then use a particle swarm algorithm to resolve it. Simulations are done under LTE networks with directional antennas in each eNodeB. Results show that coverage of the outage area can be compensated effectively and the regional service quality can be maintained meanwhile.

关键词： cellular networks cell outage compensation particle swarm algorithm downtilt adjustment

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A new algorithm for the identification of CNC geometric errors 9th

A new algorithm for the identification of CNC geometric erro...

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9th International Conference on Digital Enterprise Technology (DET) - Intelligent Manufacturing in the Knowledge Economy Era

作者： Wan, Min Liu, Yang Zhang, Weihong Northwestern Polytech Univ Sch Mech Engn Xian 710072 Shaanxi Peoples R China

This paper proposes a geometric error model for the three-axis machine tool using the homogeneous transformation matrix. An error-prediction model for the three-axis CNC machine tools is presented. This model takes the geometric errors into consideration, using the homogeneous coordinate transformation matrix(HTM) to analyze the geometric error model, which can change the tool's complex position and orientation changes in space into matrix calculations to make the model simpler and more intuitive. A simple method using the cubic polynomial functions to fit the geometric error components is proposed. Using this method, the high-order, non-linear spatial geometric problem can be transformed into algebraic equation. To large extent, the difficulty of calculation is reduced. The QC20 ball-bar is used to measure, analyze and separate all the geometric error components. A new kind of particle swarm algorithm based on the genetic algorithm is developed to solve the algebra equation. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： HTM geometric error QC20 particle swarm algorithm

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Small Signal Stability Enhancement of Multimachine Power System Stabilizer using Cuckoo Search algorithm 7

Small Signal Stability Enhancement of Multimachine Power Sys...

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7th IEEE Power India International Conference (PIICON)

作者： Chitara, Dhanraj Niazi, K. R. Swarnkar, Anil Gupta, Nikhil Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781467389624

In this paper, the Cuckoo Search algorithm (CSA) is implemented on ten-machine thirty-nine bus New England power system for optimal tuning of Power System Stabilizer (PSS) parameters. The results are compared with Genetic algorithm (GA) and particle swarm Optimization algorithm (PSOA). For this, the design problem is formulated using an eigenvalue based multiobjective function that simultaneously control the damping ratio and damping factor for designing of PSS parameters. The optimized CSA based PSS (CSAPSS) parameters are used to shift the unstable or poorly damped electromechanical modes for severe line outage to a D-shape region in the left-half of the splane. The eigenvalue analysis, time-domain simulations and performances indices are used to observe the effectiveness of CSAPSS. The robustness of CSAPSS is compared with GA based PSS (GAPSS) and PSOA based PSS (PSOAPSS) under severe disturbances.

关键词： cuckoo search algorithm genetic algorithm particle swarm algorithm power system stabilizer small signal stability

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Research of Resource Selection algorithm of Parallel Simulation System for Command Decisions Support Driven by Real-Time Intelligence 16th

Research of Resource Selection Algorithm of Parallel Simulat...

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Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

作者： Lin Jianning Jiang Jing Sun Liyang Mao Shaojie CETC 28th Sci & Technol Informat Syst Engn Lab Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Nanjing Jiangsu Peoples R China

ISBN: (纸本)9789811026638;9789811026621

At present, operational decision to the commanders rely mainly on human judgment. C4ISR system can hardly assist commanders to predict situation as well as formulate and evaluate operational plans because they are lack of effective tools and methods. Parallel simulation provide an effective to solve the above problems. However, the uncertain and incomplete battlefield intelligence in the 'battlefield fog' bring great disturbance to construct parallel simulation system. So it's an urgent problem to be solved to select the most suitable battlefield entity model based on real-time battlefield intelligence in order to increase models reliability and fidelity. In this paper we propose a parallel simulation system resource selection algorithm (PSSRSA). This algorithm adopts an improved particle swarm optimum (PSO). It also has dynamic inertia weight and an alternative method of mutation. The PSSRSA can overcome traditional PSO shortcomings that may easily fall into local optima or have slow convergence rate. Through the experiment we test the algorithm performance. The results indicate the PSSRSA that is feasible and effective in parallel simulation system resource selection.

关键词： Parallel simulation system Simulation resource selection particle swarm algorithm Dynamic inertia weight particle mutation

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Optimization Compensating Devices in the Power Supply Systems Using Population algorithms 11

Optimization Compensating Devices in the Power Supply System...

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11th International Forum on Strategic Technology (IFOST)

作者： Manusov, V. Z. Bumtsend, U. Tretyakova, E. S. NSTU Dept Power Supply Syst Novosibirsk Russia

ISBN: (纸本)9781509008551

The paper is devoted to the optimization of reactive power sources arrangement in the power supply grid of enterprises to reduce the active power losses at the lowest cost of reactive power sources. To solve the optimization problem was used the particle swarm algorithm. The paper focuses on the description of the algorithm and explanation how exactly particle swarm algorithm is used for this task. Since the particle swarm algorithm requires adjustment heuristic parameters, in the work carried out their adaptation for improvement of the algorithm effectiveness. The adaptation was implemented with the use of the genetic algorithm. It is shown that the optimization allows reducing active power losses up to 20% for the considered power supply system.

关键词： power losses reactive power compensating devices genetic algorithm particle swarm algorithm

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particle swarm algorithm applied in the layout optimization for console

Particle swarm algorithm applied in the layout optimization ...

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2nd IEEE International Conference on Advanced Computer Control

作者： Xu, Yuqing Meng, Qingxin Yang, Zhi Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China

ISBN: (纸本)9781424458479

Components on console are usually arranged by manual layout according to ergonomics standards and experiences in nuclear power plant. It is hard to balance many criterions, for it depends much on designer's judgments and preferences. This method leads to a random layout results. particle swarm optimization is a solution to complex optimization problems with constraints, and has been applied successfully in layout and optimization fields. Fitness function was structured according to the evaluating indicators which include operation frequency and order and relationships between individuals. The optimization objective was evaluated by the hand moving distance. This method could be adopted to optimize the layout of components on human-machine interface in order to ensure safety and efficiency of the system. The troubles of subjective feelings and randomness are avoided. An example of 13 components is implemented to show the layout procedure.

关键词： layout optimization particle swarm algorithm console

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Virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization

Virtual network mapping algorithm based on load balancing mu...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Jun Zhao Zhong Ma Wuhan Digital Engineering Institute China

ISBN: (纸本)9781510821279

Existing virtual network mapping algorithms does not consider resource consumption of intermediate node on communication path usually. Minimum resource consumption or shortest path of physical network is regarded as objective, thereby leading to bottleneck due to insufficient resource of intermediate node on communication path, and affecting performance of the whole physical network and subsequent success rate of virtual network. A virtual network mapping algorithm based on load balancing multi-objective particle swarm optimization is proposed in the paper aiming at the problem. Resource consumption of intermediate node is sufficiently considered in the algorithm, double balance of node load and link load is regarded as objective. Meanwhile, the optimal path of particle swarm optimization algorithm is adopted. Experiments show that the algorithm proposed in the paper can not only realize double balance of node load and link load, but also effectively improve request receiving success rate, overall resource load balance and long-term operation income.

关键词： Load Balancing particle swarm Optimization algorithm Virtual Network Mapping Load balancing intermediate node Link Load Telecommunication lines particle swarm optimization Virtual networks particle swarm algorithm Resource utilization physical network

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Parameters Optimization for Extended-range Electric Vehicle Based on Improved Chaotic particle swarm Optimization

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第9期9卷 1-10页

作者： Jiang, Yongchen Lin, Cheng Cao, Wanke Beijing Inst Technol Collaborat Innovat Ctr Elect Vehicles Beijing 100081 Peoples R China

Extended-range electric vehicle is considered to be the ideal transition type for electric vehicle. The optimal operation curve control strategy was proposed for a 12 meter-long range extended electric bus. With exponential function inertia weight adjustment and local chaos substitution, an improved chaotic particle swarm optimization algorithm was applied to optimize the key parameters of energy management strategy. Based on MATLAB/Simulink, full vehicle model and corresponding control strategy were built. The simulation results with typical city driving cycles illustrate that, comparing with standard particle swarm optimization, the new algorithm can greatly improve the convergence speed and optimizing precision, and the optimal parameters can be obtained.

关键词： Extended-range electric vehicle Energy management Simulation particle swarm algorithm

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Distributed particle swarm Optimization Using an Average Consensus algorithm

Distributed Particle Swarm Optimization Using an Average Con...

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IEEE Annual Conference on Decision and Control

作者： Yuji Wakasa Sosuke Nakaya Graduate School of Science and Engineering Yamaguchi University 2-16-1 Tokiwadai Ube Yamaguchi 755-8611 Japan

ISBN: (纸本)9781479978878

In order to improve the efficiency of distributed systems over a network, various distributed optimization algorithms have been developed recently. In particular, for optimization problems with convex and differentiable functions, sophisticated algorithms have been proposed, motivated by energy network systems such as smart grid. As an algorithm with easier implementation and wider range of applications, this paper proposes a distributed optimization algorithm that can deal with optimization problems with nonconvex and nondifferentiable functions by combining a particle swarm optimization algorithm and an average consensus algorithm. Moreover, the convergence property of the proposed algorithm is proven under mild assumptions. Through numerical experiments, the effectiveness of the proposed algorithm is illustrated.

关键词： Optimization algorithms particle swarm optimization algorithms Smart grids Distribute Distributed Systems Convexity energy grid particle swarm algorithm DIFFERENTIABLE FUNCTIONS

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Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

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COMPUTER ASSISTED SURGERY 2016年 21卷 133-142页

作者： Li, Mi Lu, Xiaofeng Wang, Xiaodong Lu, Shengfu Zhong, Ning Beijing Univ Technol Fac Informat Technol Dept Automat Beijing Peoples R China Beijing Int Collaborat Base Brain Informat & Wisd Beijing Peoples R China Beijing Key Lab MRI & Brain Informat Beijing Peoples R China Maebashi Inst Technol Dept Life Sci & Informat Maebashi Gunma Japan

The types of kernel function and relevant parameters' selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (PSO): on the one hand, the generalization ability of classifier is effectively enhanced by constructing a mixed kernel function with global kernel function and local kernel function;on the other hand, the accuracy of classification is improved through optimization for related kernel parameters based on information entropy PSO. Compared with PSO-RBF kernel and PSO-mixed kernel, the improved PSO-mixed kernel SVM can effectively improve the classification accuracy through the classification experiment on biomedical datasets, which would not only prove the efficiency of this algorithm, but also show that the algorithm has good practical application value in biomedicine prediction.

关键词： Mixed kernel function information entropy particle swarm algorithm SVM kernel function

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