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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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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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NON-LINEAR PREDICTIVE CONTROLLERS FOR THERMAL COMFORT OPTIMIZATION AND ENERGY SAVING

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IFAC Proceedings Volumes 2006年第19期39卷 87-92页

作者： Roberto Z. Freire Gustavo H.C. Oliveira Nathan Mendes Industrial and Systems Engineering Graduate Program - PPGEPS Thermal Systems Laboratory - LST Pontifical Catholic University of Parana Curitiba/PR/Brazil - Zip Code 80215-901

The present work is focused on the study of indoor thermal comfort control problem in buildings equipped with heating systems. The occupants' thermal comfort sensation is addressed here by a comfort index known as PMV (Predict Mean Vote). In this context, two control algorithms are proposed by using only-one-actuator system associated to the heating system. The methods are based on the model predictive control scheme and on the improvement of the PMV index together with energy minimization. Simulation results - obtained by using the weather data file for the city of Curitiba, Brazil - for two case studies: i ) variations between low and moderate occupants metabolic rates and ii) variations between moderate and high occupants metabolic rates are presented to validate the proposed methodology in terms of room air temperature, relative humidity and PMV control.

关键词： predictive control thermal comfort heating systems non-linear control

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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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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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Chaotic Differential Evolution Applied to Electromagnetics Optimization

Chaotic Differential Evolution Applied to Electromagnetics O...

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IEEE Conference on Electromagnetic Field Computation

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

This work presents a new global optimization algorithm based on differential evolution (DE) method and DE combined with chaotic sequences (DEC) given by logistic map. In this paper, the optimal shape design of Loney's solenoids benchmark problem is carried out by DE and DEC algorithms. The results of DE and DEC approaches are also investigated and their performance compared with those reported in the literature

关键词： Chaos Solenoids Optimization methods Evolutionary computation Stochastic processes Genetic mutations Logistics Algorithm design and analysis Design optimization Electronic mail

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Electromagnetic device optimization using improved differential evolution methods

Electromagnetic device optimization using improved different...

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12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006

作者： Coelho, Leandro Dos Santos Alotto, Piergiorgio Production and Systems Engineering Graduate Program Automation and Systems Laboratory Pontifical Catholic University of Paraná Imaculada Conccicao 1155 80215-901 Curitiba Paraná Brazil Dip. Ingegneria Eleurica University di Padova Italy

ISBN: (纸本)1424403200

Recently, evolutionary algorithms (e.g. genetic algorithms, evolutionary programming, and evolution strategies) have proven to be useful tools for the optimization of difficult problems in electromagnetics. Differential evolution (DE) is one comparatively simple variant of an evolutionary algorithm using floating-point encoding and few control parameters. This work presents improved DE algorithms based on linearly time varying control parameters, sinusoidal functions, and diversity analysis of population. © 2006 IEEE.

关键词： Electromagnets

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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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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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Electromagnetic Device Optimization using Improved Differential Evolution Methods

Electromagnetic Device Optimization using Improved Different...

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IEEE Conference on Electromagnetic Field Computation

作者： L. dos Santos Coelho P. Alotto Rcductim and Systems Engineering Gmduale Rogmm Aulmation and Systms Labaratcry PUCPR/PPGEPS/LAS Pontifical Catholic University of Paraná Curitiba Parana Brazil Production and Systems Engineering Graduate Program Aulmation and Systms Labaratory Curitiba Parana Brazil Dip. Inssgneria Eldtrica UnivslniU di I'adovn Italy

关键词： Electromagnetic devices Optimization methods Algorithm design and analysis Evolutionary computation USA Councils Writing Differential equations Encoding Convergence of numerical methods Testing

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Supply Chain Optimization Using Chaotic Differential Evolution Method

Supply Chain Optimization Using Chaotic Differential Evoluti...

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IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Heitor Silverio Lopes Production and Systems Engineering Graduate Program Automation and Systems Laboratory Pontifical Catholic University of Paraná Curitiba Parana Brazil UTFPR/CPGEI Federal Technological University of Parana Curitiba Parana Brazil

This paper describes the application of differential evolution approaches to the optimization of a supply chain. Although simplified, this supply chain included stocks, production, transportation and distribution, in an integrated production-inventory-distribution system. The supply chain problem model is presented as well as a short introduction to each evolutionary algorithm. Differential evolution (DE) is an emergent evolutionary algorithm that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. Inspired by the chaos theory, this work presents a new global optimization algorithm based on different DE approaches combined with chaotic sequences (DEC), called chaotic differential evolution algorithm. The performance of three evolutionary algorithm approaches (genetic algorithm, DE and DEC) and branch and bound method were evaluated with numerical simulations. Results were also compared with other similar approach in the literature. DEC was the algorithm that led to better results, outperforming previously published solutions. The simplicity and robustness of evolutionary algorithms in general, and the efficiency of DEC, in particular, suggest their great utility for the supply chain optimization problem, as well as other logistics-related problems.

关键词： Supply chains Optimization methods Chaos Evolutionary computation production systems Transportation Convergence Genetic algorithms Numerical simulation Robustness

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