New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

An observer-based vaccination control law for an SEIR epidemic model based on feedback linearization techniques for nonlinear systems

This paper presents a vaccination strategy for fighting against the propagation of epidemic diseases. The disease propagation is described by an SEIR (susceptible plus infected plus infectious plus removed populations) epidemic model. The model takes into account the total population amounts as a refrain for the illness transmission since its increase makes the contacts among susceptible and infected more difficult. The vaccination strategy is based on a continuous-time nonlinear control law synthesised via an exact feedback input-output linearization approach. An observer is incorporated into the control scheme to provide online estimates for the susceptible and infected populations in the case when their values are not available from online measurement but they are necessary to implement the control law. The vaccination control is generated based on the information provided by the observer. The control objective is to asymptotically eradicate the infection from the population so that the removed-by-immunity population asymptotically tracks the whole one without precise knowledge of the partial populations. The model positivity, the eradication of the infection under feedback vaccination laws and the stability properties as well as the asymptotic convergence of the estimation errors to zero as time tends to infinity are investigated.

Microscopic Model Of Nano-Scale Particles Removal In High Pressure Co2-Based Solvents

A physical model is presented to describe the kinds of static forces responsible for adhesion of nano-scale copper metal particles to silicon surface with a fluid layer. To demonstrate the extent of particle cleaning, Received in revised form equilibrium separation distance (ESD) and net adhesion force (NAF) of a regulated metal particle with different radii (10-300 nm) on the silicon surface in CO2-based cleaning systems under different pressures were simulated. Generally, increasing the pressure of the cleaning system decreased the net adhesion force between spherical copper particle and silicon surface entrapped with medium. For CO2 + isopropanol cleaning system, the equilibrium separation distance exhibited a maximum at temperature 313.15 K in the Equilibrium separation distance regions of pressure space (1.84-8.02 MPa). When the dimension of copper particle was given, for example, High pressure 50 nm radius particles, the net adhesion force decreased and equilibrium separation distance increased with increased pressure in the CO2 + H2O cleaning system at temperature 348.15 K under 2.50-12.67 MPa pressure range. However, the net adhesion force and equilibrium separation distance both decreased with an increase in surfactant concentration at given pressure (27.6 or 27.5 MPa) and temperature (318 or 298 K) for CO2 + H2O with surfactant PFPE COO-NH4+ or DiF(8)-PO4-Na+. (C) 2008 Elsevier B.V. All rights reserved.