Improvements of the Parallel Evolutionary Algorithm for Finding Solution of a System of ordinary Differential Equations
Date Issued
2012
Author(s)
Jovanovski, Jane
Abstract
The goal of our research is to evaluate the general methods
of finding solution of a system of differential equations. In
this paper we present an improved parallelization approach of
the two step parallel genetic algorithm approach that produces
an analytical solution of the system. The evaluation of the
algorithm reveals its capability to solve non-trivial systems in
very small number of generations. In order to find the best
solution, and due to the fact that the simulations are computational intensive, we use parallel grid genetic algorithms.
Using the gLite based Grid, we propose a grid genetic solution
that uses large number of computational nodes, that archives
excellent performance. This research will be the basis on our
goal of solving more complex research problems based around
the Schrodingers equation.
of finding solution of a system of differential equations. In
this paper we present an improved parallelization approach of
the two step parallel genetic algorithm approach that produces
an analytical solution of the system. The evaluation of the
algorithm reveals its capability to solve non-trivial systems in
very small number of generations. In order to find the best
solution, and due to the fact that the simulations are computational intensive, we use parallel grid genetic algorithms.
Using the gLite based Grid, we propose a grid genetic solution
that uses large number of computational nodes, that archives
excellent performance. This research will be the basis on our
goal of solving more complex research problems based around
the Schrodingers equation.
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