The objective of this paper is the interrelation of the diverse characteristics of two categories of optimization algorithms, stochastic and deterministic, as well as the exploitation of the advantages that each method presents simultaneously. It is well known that evolutionary methods are very efficient in avoiding local optima due to their stochastic nature, but there is no significant indica...

In this paper, a new control technique for nonlinear control based on hybrid neural modeling is proposed. For neural network training, a variant of the well-known gradient steepest descent method is employed where the learning rate is adapted in each iteration step in order to accelerate the speed of convergence. It is shown that appropriate selection of the learning rate results in stable trai...

The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. The GPU comp...

Milling vibration is one of the most serious factors affecting machining quality and precision. In this paper a novel hybrid error criterion-based frequency-domain LMS active control method is constructed and used for vibration suppression of milling processes by piezoelectric actuators and sensors, in which only one Fast Fourier Transform (FFT) is used and no Inverse Fast Fourier Transform (IF...

The aim of this paper is to give a uniied framework for deriving projection methods for solving systems of linear equations. We shall show that all these methods follow from a unique minimization problem. The particular cases of the methods of steepest descent, Richardson and conjugate gradients will be treated in details. Projection acceleration procedures for accelerating the convergence of a...

Binary trust-region steepest descent (BTR) and combinatorial integral approximation (CIA) are two recently investigated approaches for the solution of optimization problems with distributed binary-/discrete-valued variables (control functions). We show improved convergence results BTR by imposing a compactness assumption that is similar to theory CIA. As corollary we conclude also constitutes a...