PARALLEL GLOBAL OPTIMIZATION ALGORITHMS AND THE USE OF EVOLVENTS WITH GROWING LEVEL OF DETAILS |
| 3 | |
| 2011 |
| scientific article | 541.186 | ||
| 127-133 | global optimization, index method, parallel programming, Peano curves |
| This paper continues the development of information-statistical approach to minimization of multi-extremal functions with nonconvex constraints called the global optimization index method. The method reduces the solution of multi-dimensional problems to that of equivalent one-dimensional ones. The reduction is based on single-valued Peano curves mapping a unit interval on the real axis onto a hypercube. The scheme of building a set of Peano curves («rotated evolvents») is also used which can be effectively applied to the problem solution on a cluster with tens and hundreds of processors. The main attention is paid to the application of evolvents with different levels of details to accelerate the convergence rate of the parallel algorithm. |
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