In this paper, an improved version of compact Genetic Algorithm (M-cGA) is proposed for thinned array synthesis. By adding suitable learning scheme between probability vectors (PVs), improved cGA can effectively control the peak sidelobe (PSL) of thinned arrays. Its performances have been compared with those of a Genetic Algorthim, used alone or in conjunction with the Almost Different Set (ADS) for the optimization of several thinned arrays, showing that the M-cGA outperforms the other two schemes both in term of solution quality and computational cost.
Thinning Array using Improved Compact Genetic Algorithm
ZICH, RICCARDO;MUSSETTA, MARCO;
2013-01-01
Abstract
In this paper, an improved version of compact Genetic Algorithm (M-cGA) is proposed for thinned array synthesis. By adding suitable learning scheme between probability vectors (PVs), improved cGA can effectively control the peak sidelobe (PSL) of thinned arrays. Its performances have been compared with those of a Genetic Algorthim, used alone or in conjunction with the Almost Different Set (ADS) for the optimization of several thinned arrays, showing that the M-cGA outperforms the other two schemes both in term of solution quality and computational cost.File in questo prodotto:
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