In PID controller design, an optimization algorithm is commonly employed to search for the optimal controller parameters. The optimization algorithm is based on a specific performance criterion which is defined by an objective or cost function. To this end, different objective functions have been proposed in the literature to optimize the response of the controlled system. These functions inclu...

Neural networks are widely used as classifiers in many pattern recognition problems because of good generalization abilities, what is a crucial issue in any practical application. However, vast majority of neural network architectures demands a huge computational effort for the training process, what in turn limits such solutions from application in one important domain of pattern recognition, ...

Real life problems involve seldom only one criterion, but multiple criteria should be optimised at the same time. The criteria can even be conflicting each other, so the classical techniques used in single criteria optimization cannot be simply reused. Many ideas have been presented in the literature for addressing multiple criteria optimization problems; one of the ideas is to provide the so-c...

In Surrogate-based optimization, each optimization cycle consists of fitting a surrogate to a number of simulations at a set of design points, and performing optimization based on the surrogate to obtain one or more new design points. Algorithms like Efficient Global Optimization (EGO) [1] use uncertainty estimates available with the Kriging surrogate to guide the selection of new point(s). The...

The present paper tackles the optimization problem of double layer grids considering nonlinear behaviour. In this paper, an efficient optimization algorithm is proposed to achieve the optimization task based on the newly developed grey wolf algorithm (GWA) termed as sequential GWA (SGWA). In the framework of SGWA, a sequence of optimization processe...

Several traditional stopping criteria in Genetic Algorithms (GAs) are applied to the optimization process of a typical laminated composite plate. The results show that neither of the criteria of the type of statistical parameters, nor those of the kinds of theoretical models performs satisfactorily in determining the interruption point for the GA process. Here, considering the configuration of ...

optimization models have been used to support decision making in production planning for a long time. however, several of those models are deterministic and do not address the variability that is present in some of the data. robust optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible ...

This paper presents a stochastic direct search method for topology optimization of continuum structures. In a systematic approach requiring repeated evaluations of the objective function, the element exchange method (EEM) eliminates the less influential solid elements by switching them into void elements and converts the more influential void elements into solid resulting in an optimal 0–1 topo...

This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the multiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow to search effectively on the promising areas of the combinatorial search space are discussed. The main attention is focused on the in...

In this study, optimization models using genetic algorithms (GAs) are proposed to study the conŽguration of vowels and tone systems. As in previous explanatory models that have been used to study vowel systems, certain criteria, which are assumed to be the principles governing the structure of sound systems, are used to predict optimal vowels and tone systems. In most of the earlier studies onl...