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International Conference on Soft Computing and Intelligent Systems

作者： Ali Husseinzadeh Kashan Somayyeh Karimiyan Morteza Karimiyan Mina H. Kashan Department of Systems and Industrial Engineering Science and Research Branch Islamic Azad University Department of Earthquake Engineering International Institute of Earthquake Engineering and Seismology Department of Earthquake Engineering Science and Research Branch Islamic Azad University Department of Industrial Engineering Faculty of Engineering Tarbiat Modares University

ISBN: (纸本)9781467327428

Inspired by the competition of sport teams in a sport league, the League Championship algorithm (LCA) has been introduced recently for optimizing nonlinear continuous functions. LCA tries to metaphorically model a league championship environment wherein a number of individuals, as artificial sport teams, play in pairs in an artificial league for several weeks (iterations) based on a league schedule. Given the playing strength (fitness value) along with a team intended formation (solution) in each week, the game outcome is determined in terms of win or loss and this will serve as a basis to direct the search toward fruitful areas. At the heart of LCA is the artificial post-match analysis where, to generate a new solution, the algorithm imitates form the strengths/weaknesses/opportunities/threats (SWOT) based analysis followed typically by coaches to develop a new team formation for their next week contest. In this paper we try to modify the basic algorithm via modeling a between two halves like analysis beside the post-match SWOT analysis to generate new solutions. Performance of the modified algorithm is tested with that of basic version and the particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmark functions. Results testify that the improved algorithm called RLCA, performs well in terms of both final solution quality and convergence speed.

关键词： Numerical function optimization League championship algorithm particle swarm optimization algorithm

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Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation

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ADVANCES IN WATER RESOURCES 2011年第3期34卷 395-406页

作者： Lue, Haishen Yu, Zhongbo Zhu, Yonghua Drake, Sam Hao, Zhenchun Sudicky, Edward A. Hohai Univ Coll Hydrol & Water Resources State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Univ Waterloo Dept Earth & Environm Sci Waterloo ON N2L 3G1 Canada Univ Nevada Dept Geosci Las Vegas NV 89154 USA Univ Arizona Off Arid Lands Studies Tucson AZ 85719 USA

With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards' equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect. (C) 2010 Elsevier Ltd. All rights reserved,

关键词： Soil moisture content Root zone Richards equation Optimal parameters and extended Kalman filter data assimilation method particle swarm optimization algorithm

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Re-Scheduling of Unit Commitment Based on Customers' Fuzzy Requirements for Power Reliability

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2011年第7期E94D卷 1378-1385页

作者： Wang, Bo Li, You Watada, Junzo Waseda Univ Dept Prod Grad Sch IPS Kitakyushu Fukuoka 8080135 Japan

The development of the electricity market enables us to provide electricity of varied quality and price in order to fulfill power consumers' needs. Such customers choices should influence the process of adjusting power generation and spinning reserve, and, as a result, change the structure of a unit commitment optimization problem (UCP). To build a unit commitment model that considers customer choices, we employ fuzzy variables in this study to better characterize customer requirements and forecasted future power loads. To measure system reliability and determine the schedule of real power generation and spinning reserve, fuzzy Value-at-Risk (VaR) is utilized in building the model, which evaluates the peak values of power demands under given confidence levels. Based on the information obtained using fuzzy VaR, we proposed a heuristic algorithm called local convergence-averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. Comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

关键词： customer requirements power supply reliability fuzzy set theory fuzzy value-at-risk particle swarm optimization algorithm test systems

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New self-calibration approach to space robots based on hand-eye vision

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Journal of Central South University 2011年第4期18卷 1087-1096页

作者：刘宇刘宏倪风雷徐文福 State Key Laboratory of Robotics and System Harbin Institute of Technology Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Graduate School

To overcome the influence of on-orbit extreme temperature environment on the tool pose(position and orientation) accuracy of a space robot,a new self-calibration method based on a measurement camera(hand-eye vision) attached to its end-effector was *** the relative pose errors between the two adjacent calibration positions of the space robot,the cost function of the calibration was built,which was different from the conventional calibration *** particle swarm optimization algorithm(PSO) was used to optimize the function to realize the geometrical parameter identification of the space *** above calibration method was carried out through self-calibration simulation of a six-DOF space robot whose end-effector was equipped with hand-eye *** results showed that after calibration there was a significant improvement of tool pose accuracy in a set of independent reference positions,which verified the feasibility of the *** the same time,because it was unnecessary for this method to know the transformation matrix from the robot base to the calibration plate,it reduced the complexity of calibration model and shortened the error propagation chain,which benefited to improve the calibration accuracy.

关键词： space robot self-calibration cost function hand-eye vision particle swarm optimization algorithm

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A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4198-4205页

作者： Deng, Wu Li, Wen Yang, Xin-hua Dalian Jiaotong Univ Software Inst Dalian 116028 Peoples R China Dalian Univ Technol Minist Educ Key Lab Adv Design & Intelligent Comp Dalian Peoples R China Xiangtan Univ Key Lab Intelligent Mfg Hunan Prov Xiangtan Peoples R China

A novel hybrid optimization algorithm combining computational intelligence techniques is presented to solve the multifactor highway passenger volume prediction problem. In this paper, we can get and discretize a reduced decision table, which implies that the number of evaluation criteria such as travel quantity, fixed-asset investment, railway mileage, and waterway passenger volume are reduced with no information loss through rough set theory (RST) method. particle swarm optimization (PSO) algorithm based on the random global optimization is inducted into the network training. The PSO algorithm is used for glancing study in order to confirm the initial values, and then the back propagation neural network (BPNN) is used for given accuracy to found the PSO-BPNN model. And this reduced information is used to form a classification rule set, which is regarded as an appropriate input parameter to training PSO-BPNN model. The RST-PSO-BPNN model is obtained to forecast highway passenger volume. The rules developed by RST analysis show the best prediction accuracy if a case matches any one of the rules. The keystone of this hybrid optimization algorithm is using rules developed by RST for an object that matches any one of the rules and the PSO-BPNN model for one that does not match any of them. The effectiveness of our optimization algorithm was verified by experiments comparing the traditional gray model method. For the experiment, highway passenger volumes of China during the period 1995-2009 were selected, and for the validation, the novel hybrid optimization algorithm is reliable. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Highway passenger volume prediction Back propagation neural network Rough set Computational intelligence Hybrid optimization algorithm particle swarm optimization algorithm Discretization

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An effective global harmony search algorithm for reliability problems

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4642-4648页

作者： Zou, Dexuan Gao, Liqun Li, Steven Wu, Jianhua Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Univ S Australia Div Business Adelaide SA 5001 Australia

This paper proposes an effective global harmony search algorithm (EGHS) to solve two kinds of reliability problems: the complex (bridge) system optimization problem and the reliability-redundancy optimization problem of the overspeed protection system for a gas turbine. In general, the two problems are formulated as mixed-integer nonlinear programming problems with several constraints. The EGHS combines harmony search algorithm (HS) with concepts from the swarm intelligence of particle swarm optimization algorithm (PSO) to solve optimization problems. The proposed algorithm has been applied to two typical problems with results better than previously reported. The results have demonstrated that the EGHS has strong convergence and capacity of space exploration on solving reliability optimization problems. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Effective global harmony search algorithm Reliability problems Harmony search algorithm particle swarm optimization algorithm Convergence

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Ecological operation for Three Gorges Reservoir

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Water Science and Engineering 2011年第2期4卷 143-156页

作者： Wen-xian GUO Hong-xiang WANG Jian-xin XU Zi-qiang XIA North China University of Water Conservancy and Electric Power Zhengzhou 450011 P. R. China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai Univerxity Nanjing 210098 R R. China

The traditional operation of the Three Gorges Reservoir has mainly focused on water for flood control, power generation, navigation, water supply, and recreation, and given less attention to the negative impacts of reservoir operation on the river ecosystem. In order to reduce the negative influence of reservoir operation, ecological operation of the reservoir should be studied with a focus on maintaining a healthy river ecosystem. This study considered ecological operation targets, including maintaining the river environmental flow and protecting the spawning and reproduction of the Chinese sturgeon and four major Chinese carps. Using flow data from 1900 to 2006 at the Yichang gauging station as the control station data for the Yangtze River, the minimal and optimal river environmental flows were analyzed, and eco-hydrological targets for the Chinese sturgeon and four major Chinese carps in the Yangtze River were calculated. This paper proposes a reservoir ecological operation model, which comprehensively considers flood control, power generation, navigation, and the ecological environment. Three typical periods, wet, normal, and dry years, were selected, and the particle swarm optimization algorithm was used to analyze the model. The results show that ecological operation modes have different effects on the economic benefit of the hydropower station, and the reservoir ecological operation model can simulate the flood pulse for the requirements of spawning of the Chinese sturgeon and four major Chinese carps. According to the results, by adopting a suitable re-operation scheme, the hydropower benefit of the reservoir will not decrease dramatically while the ecological demand is met. The results provide a reference for designing reasonable operation schemes for the Three Gorges Reservoir.

关键词： ecological operation environmental flow Three Gorges Reservoir particle swarm optimization algorithm

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Grid Task Scheduling Strategy Based on particle swarm optimizationand Ant Colony optimization algorithm

Grid Task Scheduling Strategy Based on Particle Swarm Optimi...

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2010 International Conference on Advanced Measurement and Test (AMT 2010)

作者： Wei Pengcheng 1,2,a,Shi xi 2,b 1 Computer Science and Engineering College,Chongqing University,Chongqing,China 2 Department of Computer Science,Chongqing Education College Chongqing,China

Based on particle swarm optimization algorithm,this paper presents a grid scheduling optimization algorithm combing the advantages of Ant Colony optimization *** algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions,and then conducts small-area local search with Ant Colony optimization *** analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimization algorithm and particle swarm optimizationalgorithm,and can be applied to task scheduling in grid computing.

关键词： grid computing task scheduling particle swarm optimization algorithm Ant Colony optimization algorithm

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The Modeling of Steam Turbine Speed Control System Based on Radial Basis Function Neural Network

The Modeling of Steam Turbine Speed Control System Based on ...

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International Conference on Manufacturing Engineering and Automation

作者： Ren, Yong Yang, Tao Gao, Wei Li, Yanghai Huazhong Uni Sci & Tech Sch Energy & Power Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9780878492268

The model of speed control system plays an important role in power system stability studies, the non-linear properties prevent us from getting accurate mechanism model. In this paper, the radial basis function neural network with self-structuring and fast convergence is used in the modeling of steam turbine speed control system in the modeling process, also, this paper presents a method which combines particle swarm optimization algorithm and least-squares algorithm for the neural network's training, it has the property of high accuracy and fast convergence, after training, the proposed model and related training algorithm are verified by the test data of one power plant, it has proved that the neural network can be used in the modeling of the speed control system for the power system stability studies.

关键词： Steam turbine Speed control system RBF neural network particle swarm optimization algorithm Least-squares algorithm

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optimization and Analysis of Operation Strategies for Combined Cooling, Heating and Power System

Optimization and Analysis of Operation Strategies for Combin...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Li, Zhengyi Huo, Zhaoyi Yin, Hongchao Dalian Univ Technol Sch Energy & Power Engn Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian Peoples R China

ISBN: (纸本)9781424462551

The Combined cooling, heating and power (CCHP) system should be operated under the optimal scheme to meet the varying demand for cooling, heating and electricity and to reduce cost and save energy for buildings. Based on the analysis of the energy flow, a mixed integer linear program (MILP) optimal model has been developed. The objectives are to minimize the annual total cost, primary energy consumption and carbon dioxide emission, which are optimized by particle swarm optimization (PSO) algorithm for a given commercial building. Furthermore, an analysis is presented in order to show how the economical, energetic and environmental performance of a CCHP system changes under different operation strategies. This model has an actual guiding significance for the CCHP system design and operation, which will be a reference for the development of distributed energy systems.

关键词： CCHP system operation strategies optimization analysis particle swarm optimization algorithm

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