This letter proposes an algorithm that combines the improved multiobjective beluga whale optimization algorithm with the iterative Fourier transform (IFT) technique to optimize the sidelobe level (SLL) and directivity...
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This letter proposes an algorithm that combines the improved multiobjective beluga whale optimization algorithm with the iterative Fourier transform (IFT) technique to optimize the sidelobe level (SLL) and directivity of the phased array. The algorithm is derived from the traditional beluga whale optimization algorithm, based on the framework of multiobjective optimization. By improving the traditional algorithm and combining with IFT technology and opposition-based learning strategy, the population diversity is enhanced, and the optimization ability of the algorithm is improved. By optimizing the array layout, the proposed method can effectively synthesize thinned planar phased arrays with low SLL and nondegraded directivity. Some specific examples are provided to confirm the effectiveness of the proposed method.
A new numerical stochastic optimization approach with the multiagent genetic algorithm (MAGA), is firstly applied to thinned planar arraysynthesis. Furthermore, a model considered both low sidelobe level and high dir...
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ISBN:
(纸本)9781728107165
A new numerical stochastic optimization approach with the multiagent genetic algorithm (MAGA), is firstly applied to thinned planar arraysynthesis. Furthermore, a model considered both low sidelobe level and high directivity is given for thinned phased arraysynthesis. The proposed approach is utilized for the synthesis of thinned planar arrays from the aperture size of a regular square latticed 16x16 array with half-wavelength spacing. Numerical experimental results indicate the MAGA is expected to be an attractive optimization tool for thinned array synthesis and the array obtained here have the ability to consider both sidelobe and directivity as the optimization targets.
Advanced arraysynthesis problems are usually nonconvex regarding the cost functions, which are rather difficult to solve. A numerical stochastic optimization approach based on the multiagent genetic algorithm (MAGA) ...
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Advanced arraysynthesis problems are usually nonconvex regarding the cost functions, which are rather difficult to solve. A numerical stochastic optimization approach based on the multiagent genetic algorithm (MAGA) is proposed for the synthesis of highly thinnedarrays in this article. In particular, two modified techniques are introduced into the traditional MAGA, named as improved MAGA (I-MAGA), to obtain the balance between exploration and exploitation ability. The proposed approach is utilized in some advanced antenna arraysynthesis problems, including the synthesis of sum and difference patterns and synthesis of several highly thinned planar arrays with different apertures. Numerical results indicate that the proposed I-MAGA has an attractive exploration and exploitation ability in the synthesis of highly thinnedarrays. Besides, owing to the independent multiagent system, the I-MAGA is also able to obtain better solutions with a much smaller population size and less computational cost.
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