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...
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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 in...
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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. Differenti...
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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 b...
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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...
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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 psy...
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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 in...
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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.
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...
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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 Loney's solenoids benchmark problem is carried out by PSO, PSO-CA, Gaussian PSO and Gaussian PSO-CA approaches
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 in...
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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.
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 t...
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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.
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