Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation

Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation. Goudos, S., K., Siakavara, K., Samaras, T., Vafiadis, E., E., & Sahalos, J., N. IEEE Antennas and Wireless Propagation Letters, 10:670-673, 2011.
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This letter addresses the problem of designing sparse linear arrays with multiple constraints. The constraints could include the minimum and maximum distance between two adjacent elements, the total array length, the sidelobe level suppression in specified angular intervals, the main-lobe beamwidth, and the predefined number of elements. Our design method is based on differential evolution (DE) with strategy adaptation. We apply a DE algorithm (SaDE) that uses previous experience in both trial vector generation strategies and control parameter tuning. Design cases found in the literature are compared to those found by SaDE and other DE algorithms. The results show that fewer objective-function evaluations are required than those reported in the literature to obtain better designs. SaDE also outperforms the other DE algorithms in terms of statistical results. © 2011 IEEE.

@article{
 title = {Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation},
 type = {article},
 year = {2011},
 keywords = {Differential evolution (DE),genetic algorithms (GAs),linear array design,sidelobe suppression,sparse array synthesis,unequally spaced array},
 pages = {670-673},
 volume = {10},
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 created = {2020-02-29T16:57:44.488Z},
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 last_modified = {2023-02-11T18:54:04.537Z},
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 abstract = {This letter addresses the problem of designing sparse linear arrays with multiple constraints. The constraints could include the minimum and maximum distance between two adjacent elements, the total array length, the sidelobe level suppression in specified angular intervals, the main-lobe beamwidth, and the predefined number of elements. Our design method is based on differential evolution (DE) with strategy adaptation. We apply a DE algorithm (SaDE) that uses previous experience in both trial vector generation strategies and control parameter tuning. Design cases found in the literature are compared to those found by SaDE and other DE algorithms. The results show that fewer objective-function evaluations are required than those reported in the literature to obtain better designs. SaDE also outperforms the other DE algorithms in terms of statistical results. © 2011 IEEE.},
 bibtype = {article},
 author = {Goudos, Sotirios K. and Siakavara, Katherine and Samaras, Theodoros and Vafiadis, Elias E. and Sahalos, John N.},
 doi = {10.1109/LAWP.2011.2161256},
 journal = {IEEE Antennas and Wireless Propagation Letters}
}

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