Sentence examples for using a hybrid optimization from inspiring English sources

This paper proposes a piecewise modelling and parameter estimation approach for ultracapacitors using a hybrid optimization and fuzzy clustering approach.

The synthesis of the manipulator is carried out using a hybrid optimization method called GA simplex method.

The present work presents a robust methodology based mainly on generalized equivalent parameters (GEP) – for an optimal design of viscoelastic supports for rotating machinery - aiming at minimizing the unbalance frequency response of the system using a hybrid optimization technique (genetic algorithms and Nelder Mead method).

Because the problem is non-convex, we use a hybrid optimization strategy, consisting of the evolutionary-based CMA-ES algorithm (Covariance Matrix Adaptation Evolution Strategy Hansen and Ostermeier, 2001), in combination with a subsequent gradient-based optimizer.

The process configuration included a liquid-liquid extraction unit that allowed the molecular design of the extracting agent, followed by the process design of the required equipment, and finally by an optimization step using a hybrid stochastic optimization method, the differential evolution with tabu list.

In this regard, the optimization problem is solved using a hybrid evolutionary optimization approach, because of its simple structure, faster execution time and greater probability in achieving the global solution.

This paper outlines a method for optimizing the design of a lithium-ion battery pack for hybrid vehicle applications using a hybrid numerical optimization method that combines multiple individual optimizers.

This study proposes EDIGA (EDI-controls design using Genetic Algorithms), a hybrid optimization model using genetic algorithms for the design of EDI controls, that combines the search efficiency of GA with the simplicity of statistical technique, regression analysis to identify the Relative Importance of each mode of EDI controls.

Subtractive clustering was employed to transform crisp input data into fuzzy sets and radius of the clusters were tuned using a hybrid of particle swarm optimization and pattern search algorithms.

In this paper, a novel WSF framework based on composite quantile regression outlier-robust extreme learning machine (CQR-ORELM) with feature selection and parameter optimization using a hybrid population-based algorithm is developed.

Wu et al. (2009) have developed a kernel parameter-optimization technique using a hybrid model of GA and SVR.