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International Journal of Antennas and Propagation
Volume 2016, Article ID 1829458, 7 pages
Research Article

Array Pattern Synthesis Using Particle Swarm Optimization with Dynamic Inertia Weight

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Received 22 January 2016; Accepted 16 March 2016

Academic Editor: Jaume Anguera

Copyright © 2016 Chuang Han and Ling Wang. 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.


A Feedback Particle Swarm Optimization (FPSO) with a family of fitness functions is proposed to minimize sidelobe level (SLL) and control null. In order to search in a large initial space and converge fast in local space to a refined solution, a FPSO with nonlinear inertia weight algorithm is developed, which is determined by a subtriplicate function with feedback taken from the fitness of the best previous position. The optimized objectives in the fitness function can obtain an accurate null level independently. The directly constrained SLL range reveals the capability to reduce SLL. Considering both element positions and complex weight coefficients, a low-level SLL, accurate null at specific directions, and constrained main beam are achieved. Numerical examples using a uniform linear array of isotropic elements are simulated, which demonstrate the effectiveness of the proposed array pattern synthesis approach.