Abstract
A parallel global optimization algorithm based on the density clustering technique by Boendeb is described. Two versions, the synchronous and the asynchronous one, are proposed and numerical experiments are carried out using a simulation environment of a parallel MIMD machine. A comparison between the sequential and parallel versions of the algorithm, both in term of time and of number of function evaluations, shows that the asynchronous one is very promising for machines based on MIMD architecture
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