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2006 IEEE Congress on Evolutionary Computation, CEC 2006

作者： Dos Coelho, Leandro Santos Mariani, Viviana Coceo Pontifical Catholic University of Paraná Production and Systems Eng. Graduate Program Automation and Systems Laboratory Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil Pontifical Catholic University of Paraná Mechanical Engineering Graduate Program Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil

ISBN: (纸本)0780394879

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the swarm intelligence theory, this paper discusses the use of PSO approaches using an operator and based on the Gaussian probability distribution function as a population space of a cultural algorithm, called cultural Gaussian PSO (GPSO-CA). Cultural algorithms are mechanisms that incorporate domain knowledge obtained during the evolutionary process, which increase the efficiency of the search process. These approaches are employed in a well-studied continuous optimization problem of mechanical engineering design. © 2006 IEEE.

关键词： Global optimization

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Modeling and analysis of artificial transportation system based on multi-agent technology

Modeling and analysis of artificial transportation system ba...

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： He, Feng Miao, Qinghai Li, Yuantao Wang, Fei-Yue Tang, Shuming IEEE Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences CO 100080 Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States Institute of Automation Shandong Academy of Sciences Jinan 250014 China

ISBN: (纸本)1424400945

This paper presents the multi-agent architecture for artificial transportation system. In this architecture, Petri net is used as basic model to represent agents. At an intersection, the agents are divided into two groups: one for the traffic-signal and the other for the vehicle flow, which are integrated to represent the behavior of this intersection. In addition, those agents can be used as the modularity to represent urban network of more scale. To coordinate different intersection agents, game theory is used to design coordination strategy between agents. The iterated elimination of strictly dominated strategies algorithm is presented to find Nash equilibrium. © 2006 IEEE.

关键词： Transportation

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Modeling interactions in artificial transportation systems using petri net

Modeling interactions in artificial transportation systems u...

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： Zhu, Fenghua Wang, Zhixue Wang, Fei-Yue Tang, Shuming IEEE Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences CO 100080 Beijing China Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States Institute of Automation Shandong Academy of Sciences Jinan 250014 China

ISBN: (纸本)1424400945

Agents and interactions between them are two key roles in modeling artificial transportation systems. This paper presents a formal theory to describe interactions between agents, which, together with the well developed models to describe components as agents, completes the first step towards establishing the formal structure for artificial transportation systems. By describing individual agents and the interactions between them as independent simple sequence processes and directive communication channels respectively, we models the whole system as one sequential communications system, which is one special type of Petri net. The theory is flexible enough to model diversified interactions between agents, and the resultant model possesses favorable properties. A case study of modeling road network has been conducted for the purpose of illustration. © 2006 IEEE.

关键词： Transportation

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An agent-based controller for vehicular automation

An agent-based controller for vehicular automation

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ITSC 2006: 2006 IEEE Intelligent Transportation systems Conference

作者： He, Feng Wang, Fei-Yue Tang, Shuming IEEE Key Laboratory for Complex Systems and Intelligence Science Chinese Academy of Sciences Beijing China Program for Advanced Research in Complex Systems Department of Systems and Industrial Engineering University of Arizona Tucson AZ 85719 United States Institute of Automation Shandong Academy of Sciences Jinan 250014 China

ISBN: (纸本)1424400945

In this paper, we present a design of controller for vehicular automation based on mobile agent technology. In this approach, the control goal is conducted by coordinating active or default agents based on sensory feedback and vehicle conditions. A hosting mechanism determines which agent has the right to control the vehicle and make the decision. Those agents are divided into two groups: default agents which are developed by fuzzy rules, and active agents which are produced by neural networks. Fuzzy agents locate in the vehicle and neural network agents locate in remote server. Two types of agents have similar function but different performance, so those agents produced by neural network can transfer back to the vehicle after learning to replace default fuzzy agents according to mobile agent technology. The performance of the proposed technique is illustrated by simulation studies of a vehicle longitudinal control system. © 2006 IEEE.

关键词： Land vehicle propulsion

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Particle Swarm Optimization with Quasi-Newton Local Search for Solving Economic Dispatch Problem

Particle Swarm Optimization with Quasi-Newton Local Search f...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Viviana Cocco Mariani Production and Systems Engineering Graduate Program Automation and Systems Laboratory Pontifical Catholic University of Paraná Curitiba Brazil

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the swarm intelligence theory, this paper discusses the use of PSO with a Quasi-Newton (QN) local search method. The PSO is used to produce good potential solutions, and the QN is used to fine-tune of final solution of PSO. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects.

关键词： Particle swarm optimization Power generation Power generation economics Fuel economy Cost function Equations Power system economics System testing Character generation Constraint optimization

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A genetic algorithm for the optimization of cable routing

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systems and Computers in Japan 2006年第7期37卷 61-71页

作者： Ma, Xuan Iida, Kazuhiro Xie, Mengchun Nishino, Junji Odaka, Tomohiro Ogura, Hisakazu Graduate School of Engineering University of Fukui Fukui City 910-8507 Japan Wakayama National College of Technology Gobo City 644-0023 Japan Faculty of Automation and Information Engineering Xi an University of Technology B Techno College Department of Electrical and Computer Engineering Wakayama National College of Technology Information Processing Society of Japan China Mechanical Design Society Department of Systems Engineering University of Electro-Communications Japan Society for Fuzzy Theory and Intelligent Informatics Japanese Academy of Facial Studies Japan Society of Kansei Engineering Graduate Program of Nuclear Power and Energy Safety Engineering Graduate School of Engineering Fukui University IEEE CS ACM IPSJ IEICE JSMBET Department of Human and Artificial Intelligent Systems JSAI Japan SOFT IEEE

In this paper the authors propose a structural method for a genetic algorithm (GA) for the optimization problem of cable routing in which cables have to be laid optimally. When there are no limits on the layout routes, the optimal routes for the individual cables can be found using the Dijkstra method in a finite discrete graph. However, when there are limits on the capacity in the layout routes, optimization cannot be performed using the Dijkstra method. The proposed GA is a two-level hierarchical GA that uses chromosome coding involving two levels. The routes for each cable and the combinations of cable routes are defined respectively, and layout route selection is optimized overall through genetic operations involving these levels. Block crossover and block mutation are used as genetic operations in the first level. In addition, a method to take advantage of lethal genes, which do not satisfy the constraints and are generated in the second level is used. Computer simulations confirm that the two-level hierarchical GA with these genetic operations functions effectively for the optimization problem of cable routing. © 2006 Wiley Periodicals, Inc.

关键词： Electric cable laying

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Use of Cultural Particle Swarm Optimization for Loney's Solenoids Design

Use of Cultural Particle Swarm Optimization for Loney's Sole...

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IEEE Conference on Electromagnetic Field Computation

作者： L. dos Santos Coelho V. Cocco Mariani Production and Systems Engineering Graduate Program Automation and Systems Laboratory PUCPR/PPGEPS/LAS Pontifical Catholic University of Paraná Curitiba Brazil Mechanical Engineering Graduate Program PUCPR/PPGEM Pontifical Catholic University of Paraná Curitiba Brazil

This work presents the use of particle swarm optimization (PSO) techniques with the particles' population space based on normative knowledge of cultural algorithms (CA). In this work, the optimal shape design of L... 详细信息

关键词： Cultural differences Particle swarm optimization Solenoids Optimization methods Design optimization Algorithm design and analysis Gaussian distribution Probability distribution Random number generation Equations

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An Efficient Particle Swarm Optimization Approach Based on Cultural Algorithm Applied to Mechanical Design

An Efficient Particle Swarm Optimization Approach Based on C...

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Congress on Evolutionary Computation

作者： L. dos Santos Coelho V.C. Mariani Production and Systems Engineering Graduate Program Automation and Systems Laboratory PPGEPS/CCET/PUCPR Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

关键词： Particle swarm optimization Cultural differences Algorithm design and analysis Probability distribution Design optimization Equations Design engineering Mechanical engineering Random number generation Psychology

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Nonlinear System Identification Based on B-Spline Neural Network and Modified Particle Swarm Optimization

Nonlinear System Identification Based on B-Spline Neural Net...

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International Joint Conference on Neural Networks (IJCNN)

作者： L. dos Santos Coelho R.A. Krohling Production and Systems Engineering Graduate Program Automation and Systems Laboratory PPGEPS/CCET/PUCPR Pontifical Catholic University of Paraná Parana Brazil Chair for Control Systems Engineering RST Faculty of Electrical Engineering and Information Technology University of Dortmund Dortmund Germany

Artificial neural networks, in particular, feedforward multilayer networks and basis function networks, have gradually established themselves as a usual tool in approximating complex nonlinear systems. B-spline networks, a type of basis function neural network, are normally trained by gradient-based methods, which may fall into local minima during the learning phase. In order to overcome the drawbacks encountered by conventional learning methods, particle swarm optimization - a swarm intelligence methodology - can provide a stochastic global search of B-spline networks for nonlinear system identification. In this paper, a modified particle swarm optimization algorithm using Gaussian and Cauchy probability distributions are applied to adjust the control points of B-spline neural networks. Simulation results for the identification of Rossler systems are provided and demonstrate the effectiveness and robustness of the proposed identification scheme.

关键词： Nonlinear systems Spline Neural networks Particle swarm optimization Artificial neural networks Multi-layer neural network Learning systems Stochastic systems Probability distribution Robustness

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Neural Networks, Fuzzy System, and Linear Models in Forecasting Exchange Rates: Comparison and Case Studies

Neural Networks, Fuzzy System, and Linear Models in Forecast...

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International Joint Conference on Neural Networks (IJCNN)

作者： A.A.P. Santos L. dos Santos Coelho Graduate Program in Economics Federal University of Santa Catarina Santa Catarina Brazil Production and Systems Engineering Graduate Program Automation and Systems Laboratory PPGEPS/CCET/PUCPR Pontifical Catholic University of Paraná Parana Brazil

Artificial neural networks and fuzzy systems, have gradually established themselves as popular tools in approximating complicated nonlinear systems and time series forecasting. This paper investigates the hypothesis that the nonlinear mathematical models of multilayer perceptron and radial basis function neural networks and the Takagi-Sugeno (TS) fuzzy system are able to provide a more accurate out-of-sample forecast than the traditional autoregressive moving average (ARMA) and ARMA generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) linear models. Using series of Brazilian exchange rate (R$/US$) returns with 15 min., 60 min., 120 min., daily and weekly basis, the out-pf-sample one-step-ahead forecast performance is compared. Results indicate that forecast performance is strongly related to the series' frequency and the forecasting evaluation shows that nonlinear models perform better than their linear counterparts. In the trade strategy based on forecasts, nonlinear models achieve higher returns when compared to a buy-and-hold strategy and to the linear models.

关键词： Neural networks Fuzzy systems Predictive models Exchange rates Autoregressive processes Artificial neural networks Mathematical model Nonlinear systems Multilayer perceptrons Radial basis function networks

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