A Hybrid Taguchi Binary Particle Swarm Optimization for Antenna Designs

Jia, Xingning; Lu, Guizhen

doi:10.1109/lawp.2019.2924247

Cited by 56 publications

(24 citation statements)

References 9 publications

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“…And then, the optimization of period sequence becomes a multivariable optimization problem. Various multi-objective optimization methods, such as the genetic algorithm (GA) [14,15] and the particle swarm optimization (PSO) [16,17], have been investigated. But the algorithms are difficult to be used in optimizing the ITR period sequence directly.…”

Section: Introductionmentioning

confidence: 99%

Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation

Liu

Zhao

et al. 2021

EURASIP J. Adv. Signal Process.

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The interrupted transmitting and receiving (ITR) can be used in anechoic chamber to solve the coupling between the transmitted and reflected signals. When the ITR periods are random, the fake peaks in high-resolution range profile (HRRP) of ITR echo can be reduced. Then, by utilizing the piecewise sparse property of ITR echo, the HRRP is reconstructed based on compressive sensing (CS). However, the periods of ITR determine the restricted isometry property (RIP) condition and the HRRP reconstruction performance. In order to improve the HRRP reconstruction performance, the ITR period sequence optimization method based on a genetic algorithm (GA) is proposed in this paper. The correlation coefficient of the sensing matrix columns is minimized after optimization so that the RIP of the sensing matrix can be well satisfied. Simulation and comparison results illustrate that the optimization method converges fast and the HRRP reconstruction performance is improved with the optimized ITR periods.

show abstract

Section: Introductionmentioning

confidence: 99%

Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation

Liu

Zhao

et al. 2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…And then, the optimization of periods sequence becomes a multivariable optimization problem. Various multi-objective optimization methods, such as the genetic algorithm (GA) [14][15] and the particle swarm optimization (PSO) [16][17], have been investigated. But the algorithms are difficult to be used in optimizing the ITR periods sequence directly.…”

Section: Introductionmentioning

confidence: 99%

Pulse Radar Randomly Interrupted Transmitting and Receiving Optimization Based on Genetic Algorithm in Radio Frequency Simulation

Liu

Zhao

et al. 2021

Preprint

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The interrupted transmitting and receiving (ITR) can be used in anechoic chamber to solve the coupling between the transmitted and reflected signals. When the ITR periods are random, the fake peaks in high resolution range profile (HRRP) of ITR echo can be reduced. Then, by utilizing the piecewise sparse property of ITR echo, the HRRP is reconstructed based on compressive sensing (CS). However, the periods of ITR determine the restricted isometry property (RIP) condition and the HRRP reconstruction performance. In order to improve the HRRP reconstruction performance, the ITR periods sequence optimization method based on genetic algorithm (GA) is proposed in this paper. The correlation coefficient of the sensing matrix columns is minimized after optimization so that the RIP of sensing matrix can be well satisfied. Simulation and comparison results illustrate that the optimization method converges fast and the HRRP reconstruction performance is improved with the optimized ITR periods.

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“…Among the various EAs, the most used, in particular for the array pattern synthesis, are the Genetic Algorithm (GA) [3], [4], the Differential Evolution (DE) [5] and Particle Swarm Optimization (PSO) [6]. To improve the algorithms performance, especially related to their convergence capability, some hybrid approaches were also proposed: in [7] a technique obtained by hybridizing GA and PSO is presented, while in [8] and [9] the PSO alone or in conjunction with the GA is further hybridized with the Taguchi method. In [10], the genetic algorithm is combined with a local optimization approach, and in [11], [12] the convex programming is used to increase the performance of the single objective or the multi-objective PSO, respectively.…”

Section: Introductionmentioning

confidence: 99%

Social Network Optimization Based Procedure for Beam-Scanning Reflectarray Antenna Design

Niccolai

Beccaria

Zich

et al. 2020

IEEE Open J. Antennas Propag.

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Evolutionary algorithms can be successfully exploited for carrying on an effective design of beam-scanning passive reflectarrays, even if the problem is highly non-linear and multimodal. In this paper, the Social Network Optimization (SNO) algorithm has been used for assessing an effective design procedure of a beam-scanning passive reflectarray (RA). For exploiting at most the optimization capabilities of SNO, the entire optimization environment has been deeply analyzed in all its parts. The performance of SNO and the beam-scanning capabilities of the optimized RA have been assessed through the comparison with other well established Evolutionary Algorithms.

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A Hybrid Taguchi Binary Particle Swarm Optimization for Antenna Designs

Cited by 56 publications

References 9 publications

Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation

Pulse radar randomly interrupted transmitting and receiving optimization based on genetic algorithm in radio frequency simulation

Pulse Radar Randomly Interrupted Transmitting and Receiving Optimization Based on Genetic Algorithm in Radio Frequency Simulation

Social Network Optimization Based Procedure for Beam-Scanning Reflectarray Antenna Design

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