A modified real GA for the sparse linear array synthesis with multiple constraints

This paper describes a modified real genetic algorithm (MGA) for the synthesis of sparse linear arrays. The MGA has been utilized to optimize the element positions to reduce the peak sidelobe level (PSLL) of the array. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. Unlike standard GA using fixed corresponding relationship between the gene variables and their coding, the MGA utilized the coding resetting of gene variables to avoid infeasible solution during the optimization process. Also, the proposed approach has reduced the size of the searching area of the GA by means of indirect description of individual. The simulated results confirming the great efficiency and the robustness of this algorithm are provided in this paper.