• No se han encontrado resultados

Global numerical optimization with a bi-population particle swarm optimizer

N/A
N/A
Protected

Academic year: 2017

Share "Global numerical optimization with a bi-population particle swarm optimizer"

Copied!
12
0
0

Texto completo

Loading

Figure

Figure 1 illustrates this concept.
Figure 2: Pseudo-code of Bi-PSO
Table 1: Comparison of the best values obtained by our Bi-PSO and the Differential Evolution (DE).
Table 2: Best Values obtained with Bi-PSO and DE.
+3

Referencias

Documento similar

It is interesting to observe that these figures, derived from a numerical search as defined by the algorithm, are indeed quite intuitive and further confirm the results from

Government policy varies between nations and this guidance sets out the need for balanced decision-making about ways of working, and the ongoing safety considerations

Unitary evolution of quantum systems, which was described by the Schr¨ odinger equation in the algebraic setting, is now governed by a Hamiltonian vector fields with respect to

In this context, the algorithm was conceived to treat with finite state machines and other similar machines. The main features of evolutionary programming ap- peared to fulfill

We measure the differential cross sections dσ=dE T with respect to the photon transverse energy E T and dσ=dΔR μγ with respect to the separation of the photon from the nearest

Moreover, the network of D molecules is shifted by 0.14 nm with respect to the one of D molecules (which coincides with the network of M molecules). Our XPS results reveal the

The disadvantage of HALO4 with respect to ALO4 is that the localization algorithm has a greater complexity, needing a minimization process to obtain the position of the

With respect to the product of area and the global heat transfer coefficient air cooler is shown how the product of area and the global heat transfer coefficient increases