In this paper, an improved binary brain storm optimization (ImBBSO) method is presented for pattern synthesis of linear thinned array. To avoid local convergence , obtain the global optimum, on the basic of original b...
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In this paper, an improved binary brain storm optimization (ImBBSO) method is presented for pattern synthesis of linear thinned array. To avoid local convergence , obtain the global optimum, on the basic of original binary brain storm optimization (BBSO) algorithm, the designed method mainly modifies three aspects, containing the clustering centers selection, the forced bit transposition mechanism and the survival of population. The interactions of these three operations drive optimization variables to move toward a better direction. During optimization process, the formulated cost function balances the relationship among the number of active elements, the peak sidelobe level and the half power beam width, which are eventually in the best balance state after completing iterations. Besides, the subarray partition strategy is also integrated into the proposed optimization framework with slight amendments, which can be applied to many engineering fields with space restrictions. Finally, several numerical examples demonstrate the effectiveness and applicability of the proposed method compared with other existing results of state-of-the-art methods, whether in benchmarking functions or lineararray application.
The array thinning technique can greatly reduce the number of the array elements while keeping the performance of the array almost the same. However, the existing algorithms have slow convergence rates and are easy to...
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The array thinning technique can greatly reduce the number of the array elements while keeping the performance of the array almost the same. However, the existing algorithms have slow convergence rates and are easy to fall into local optimum. To improve the optimisation performance, a hybrid method based on improved genetic algorithm (GA) and iterative Fourier transform (IFT) technique for linear thinned array is proposed in this study. The population is divided into improved GA group and IFT group according to the convergence of the population and different operations can be done paralleled to generate offspring in each iteration. In the improved GA processing, adaptive crossover rate and mutation rate are used. The mechanism that keeps the fill factor stable is removed for larger search range. The IFT processing is executed paralleled for fast convergence velocity. The proposed hybrid method can obtain the fast convergence velocity and avoid being trapped into the local optimum by the combination of the two approaches. Several examples are simulated to validate the performance of the proposed method.
A new strategy based on compact Genetic Algorithm (cGA) for the synthesis of linear thinned arrays is here proposed. In order to exploit all available knowledge from Almost Different Set (ADS) for thinnedarray to obt...
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ISBN:
(纸本)9788890701832;9781467321877
A new strategy based on compact Genetic Algorithm (cGA) for the synthesis of linear thinned arrays is here proposed. In order to exploit all available knowledge from Almost Different Set (ADS) for thinnedarray to obtain very low peak sidelobe level (PSL) suitable probability vectors to represent the solutions have been introduced. As a proof of concept, several thinnedarrays have been synthesized and the obtained results are here discussed.
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