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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...

引用

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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Electromagnetic device optimization using improved differential evolution methods

Electromagnetic device optimization using improved different...

引用

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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A multivariable coupling design for variable structure control using particle swarm optimization

A multivariable coupling design for variable structure contr...

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2006 ASME International Mechanical engineering Congress and Exposition, IMECE2006

作者： Dos Santos Coelho, Leandro Mariani, Viviana Cocco Pontifical Catholic University of Parana Production and Systems Engineering Graduate Program PPGEPS / CCET / PUCPR Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil Pontifical Catholic University of Parana Mechanical Engineering Graduate Program PPGEM / CCET/ PUCPR Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil

ISBN: (纸本)0791837904

This paper presents a new discrete-time sliding-mode control design for multiple-input multi-output (MIMO) systems with tuning parameters by particle swarm optimization (PSO). PSO is a kind of evolutionary algorithm based on a population of individuals and motivated by the simulation of social behavior instead of the survival of the fittest individual. Several control algorithms are presented, two decoupling design and six new approaches of the coupling design of sliding-mode control without the necessity of calculate the process interactor matrix. SMC needs a design tool for parameter configuration and efficient practically to deal with multivariable processes. Simulations are carried out using both decoupling and coupling discrete-time SMC designs. Results shown that the new proposed approach for designing the discrete-time coupling SMC is a powerful tool and it performs better than the decoupling design, usually utilized in MIMO process. The simulations are assessed on a robotic manipulator of two degree-of-freedom (2-DOF), that constitute a MIMO nonlinear coupling dynamic system, with treatment of payload mass and link length variations. Simulation results show that the application of this control strategy effectively improve the trajectory tracking precision of position and velocity variables. Copyright © 2006 by ASME.

关键词： Sliding mode control

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Discrete variable structure control based on optimization by cultural differential evolution approach

Discrete variable structure control based on optimization by...

引用

2006 ASME International Mechanical engineering Congress and Exposition, IMECE2006

作者： Dos Santos Coelho, Leandro Mariani, Viviana Cocco Pontifical Catholic University of Parana Production and Systems Engineering Graduate Program PPGEPS / CCET/ PUCPR Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil Pontifical Catholic University of Parana Mechanical Engineering Graduate Program PPGEM / CCET/ PUCPR Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil

ISBN: (纸本)0791837904

Advanced conceptions to design industrial control systems are, in general, dependent of mathematical models of the controlled process. Also, the task of the controllers is to achieve an optimum performance when facing with various types of disturbances that are, sometimes, unknown in practical applications. An advanced methodology for control design is the variable structure control (VSC) with sliding mode. This paper considers the non-linear control of industrial processes based on self-tuning discrete VSC technique. A novel and systematic VSC design methodology is proposed, which integrates an estimator based on recursive least square algorithm, a discrete quasi-sliding surface and a optimization method. Contrary to the trial and error selection of the variable structure feedback gains reported in the literature, the selection in present work is done using differential evolution (DE) optimization, an evolutionary computation technique, with a new operator based on normative knowledge of cultural algorithms. The proposed design has been applied to a control valve described by a non-linear Wiener model. Numerical simulation results reveal that the new DE approach is applicable and promising for the VSC design. Copyright © 2006 by ASME.

关键词： Optimal control systems

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Mechanical design using quantum-behaved particle swarm optimizer with exponential mutation operator

Mechanical design using quantum-behaved particle swarm optim...

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2006 ASME International Mechanical engineering Congress and Exposition, IMECE2006

作者： Dos Santos Coelho, Leandro Marian, Viviana Cocco Production and Systems Engineering Graduate Program PPGEPS / CCET / PUCPR Pontifical Catholic University of Parana Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil Mechanical Engineering Graduate Program PPGEM / CCET/ PUCPR Pontifical Catholic University of Parana Imaculada Conceicao 1155 80215-901 Curitiba Parana Brazil

ISBN: (纸本)0791837904

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviours of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents new Quantum-behaved PSO (QPSO) approaches using mutation operator with exponential probability distribution. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design. Copyright © 2006 by ASME.

关键词： Quantum theory

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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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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...

引用

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

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

引用

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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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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