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International Journal of Antennas and Propagation
Volume 2019, Article ID 1064103, 13 pages
Research Article

Multiobjective Synthesis of Linear Arrays by Using an Improved Genetic Algorithm

School of Electronic Science and Engineering, Jilin University, 130012 Changchun, China

Correspondence should be addressed to Bo Yang; moc.621@llaobgnay

Received 18 July 2018; Revised 5 September 2018; Accepted 4 April 2019; Published 22 July 2019

Academic Editor: Shiwen Yang

Copyright © 2019 Bo Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this paper, an improved genetic algorithm with dynamic weight vector (IGA-DWV) is proposed for the pattern synthesis of a linear array. To maintain the diversity of the selected solution in each generation, the objective function space is divided by the dynamic weight vector, which is uniformly distributed on the Pareto front (PF). The individuals closer to the dynamic weight vector can be chosen to the new population. Binary- and real-coded genetic algorithms (GAs) with a mapping method are implemented for different optimization problems. To reduce the computation complexity, the repeat calculation of the fitness function in each generation is replaced by a precomputed discrete cosine transform matrix. By transforming the array pattern synthesis into a multiobjective optimization problem, the conflict among the side lobe level (SLL), directivity, and nulls can be efficiently addressed. The proposed method is compared with real number particle swarm optimization (RNPSO) and quantized particle swarm optimization (QPSO) as applied in the pattern synthesis of a linear thinned array and a digital phased array. The numerical examples show that IGA-DWV can achieve a high performance with a lower SLL and more accurate nulls.