Modified PSO Algorithm for Planar Arrays Pattern Optimization

QIU Yongbin; HUANG Xianlin; ZHANG Shuchun; JIANG Wei

doi:10.11999/JEIT170167

Volume 39 Issue 10

Oct. 2017

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QIU Yongbin, HUANG Xianlin, ZHANG Shuchun, JIANG Wei. Modified PSO Algorithm for Planar Arrays Pattern Optimization[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2340-2345. doi: 10.11999/JEIT170167

Citation:

QIU Yongbin, HUANG Xianlin, ZHANG Shuchun, JIANG Wei. Modified PSO Algorithm for Planar Arrays Pattern Optimization[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2340-2345. doi: 10.11999/JEIT170167

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Modified PSO Algorithm for Planar Arrays Pattern Optimization

doi: 10.11999/JEIT170167

1.
2.
(Flight Simulation Technology Institute, Air Force Harbin Flight Academy, Harbin 150001, China)

Funds:

The National Natural Science Foundation of China (61273095)

Received Date: 2017-02-27
Rev Recd Date: 2017-05-12
Publish Date: 2017-10-19

Abstract

Abstract

Particle Swarm Optimization (PSO) is an efficient technology to synthesize desired pattern of antenna arrays. But in some complicated cases, the optimizer will fail because the optimum particles fall into local best solutions and the convergence is so bad, especially for nonlinear optimization of large planar arrays pattern. To solve this problem, a modified optimizer is presented to improve the convergence of traditional PSO by means of initializing the particles with analytical values rather than random values. For any given desired pattern, the corresponding aperture weights can be derived by matrix operations and these weights are then used as a particles initial values while other particles are still initialized randomly. By this initialization, an efficient estimation of the optimum particles initial values can be achieved before the beginning of all particles searching process. After that the standard PSO iterations work as usual. The simulation results prove that this modified optimizer converges more rapidly and deeply than the traditional PSO and more satisfying global solutions and desired pattern could be obtained.
- Active Electronically Scanned Array (AESA),
- Planar array,
- Sidelobes interference,
- Pattern optimization,
- Particle Swarm Optimization (PSO)

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References(16)

References

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