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Performance comparison of hybrid electromagnetism-like mechanism algorithms with descent method

Miyajima H., Shigei N., Miyajima H.

Journal of Artificial Intelligence and Soft Computing Research

2015

Vol. 5, No. 4

271--282

Electromagnetism-like Mechanism (EM) method is known as one of metaheuristics. The basic idea is one that a set of parameters is regarded as charged particles and the strength of particles is corresponding to the value of the objective function for the optimization problem. Starting from any set of initial assignment of parameters, the parameters converge to a value including the optimal or semi-optimal parameter based on EM method. One of its drawbacks is that it takes too much time to the convergence of the parameters like other meta-heuristics. In this paper, we introduce hybrid methods combining EM and the descent method such as BP, k-means and FIS and show the performance comparison among some hybrid methods. As a result, it is shown that the hybrid EM method is superior in learning speed and accuracy to the conventional methods.

A digital system to measure frequency and amplitude of power grid voltage

Borkowski D.

Jakość i Użytkowanie Energii Elektrycznej

2001

T. 7, z. 2

51-56

The paper presents complete, software-hardware solution of real-time measurement of frequency and amplitude of energic voltage signal besed on DSP processor. the hardware contains DSP board with A/D converter and 8051-based daughterboard with LCD and small keyboard. The software consists algorithm approximating sampled data of the valtage signed by a sinus function. This algorithm uses modified steepest descent method for error function minimization. An innovative modification was developed in order to reduce direction oscillations in the steepest cescent method. The modification increases the speed of function minimization by factor between 4 and 22. Results such as speed and accuracy are far better than in counting method of frequency measurement. Speed of about one measurement per secont was achieved using a 40 MHz TMS320c32 DSP processor. The maximum relative error of frequency measurement amounted to 0,56% at 60 samples of measured signal and 0,18% at 100 data samples.

Approximate relaxed descent method for optimal control problems

Chryssoverghi I., Coletsos J., Kokkinis B.

Control and Cybernetics

2001

Vol. 30, no 4

385-404

We consider an optimal control problem for systems governed by ordinary differential equations with control constraints. Since no convexity assumptions are made on the data, the problem is reformulated in relaxed form. The relaxed state equation is discretized by the implicit trapezoidal scheme and the relaxed controls are approximated by piecewise constant relaxed controls. We then propose a combined descent and discretization method that generates sequences of discrete relaxed controls and progressively refines the discretization. Since here the adjoint of the discrete state equation is not defined, we use, at each iteration, an approximate derivative of the cost functional defined by discretizing the continuous adjoint equation and the integral involved by appropriate trapezoidal schemes. It is proved that accumulation points of sequences constructed by this method satisfy the strong relaxed necessary conditions for optimality for the continuous problem. Finally, the computed relaxed controls can be easily approximated by piecewise constant classical controls.