Improved Hybrid Real-Binary PSO Using Modified Learning and Restarting Methods for Planar Microwave Absorber Designs

Jia, Xingning; Qin, Guoche; Li, Chunshu

doi:10.1109/access.2023.3244792

Cited by 2 publications

(4 citation statements)

References 29 publications

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“…Te detailed expression, including the upper and lower bounds of each function, can be found in [12]. All functions are tested on the assumption that partial real variables are represented by a binary string with a quantization error of less than 2 × 10 − 4 , as suggested in [6].…”

Section: Parameters Selectionmentioning

confidence: 99%

“…Te result of each experiment is a mean rank value, which can be calculated as follows: frst, the mean values of 23 benchmark functions with 100 independent runs are obtained by HPSO [4], IHPSO [6], GA, HGWO [13], BEO [14], and HDE n , where HDE n denotes the HDE algorithm with the combination of CPs in the n th experiment and 1 ≤ n ≤ N 0 . Second, the rank of the mean value of the m th benchmark function obtained by the HDE n algorithm, denoted as r mn , is calculated.…”

Section: Parameters Selectionmentioning

confidence: 99%

“…Two cases, denoted as case 1 and case 2, are optimized using fve algorithms, namely, HDE [5], HPSO [4], IHPSO [6], HDE N1 , and HDE N2 , respectively. Cases 1 and 2 involve minimizing the refection coefcient under normal incidence within the low-frequency band (0.2-2 GHz) and high-frequency band (2-8 GHz) with the constraint T d � 5 mm.…”

Section: Applications In Electromagnetic Optimizationmentioning

confidence: 99%

“…Many hybrid evolution algorithms have been proposed to address problems involving both real and binary parameters [1][2][3][4][5][6], and some of them have been utilized to solve antenna optimization problems such as antenna array synthesis [4,5], material selection of microwave absorbers [6], and pixelated antenna design [4,5]. A hybrid real-binary differential evolution (HDE) algorithm was proposed for antenna design in [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identification of Control Parameters Using Taguchi Method for Hybrid Real-Binary Differential Evolution Algorithm and Its Applications in Electromagnetic Optimization

Du,

Wang,

Wang

et al. 2023

International Journal of Antennas and Propagation

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The hybrid real-binary differential evolution (HDE) algorithm has been proficient in addressing electromagnetic optimization problems (EOPs) involving both real and binary variables. However, its optimization performance on different control parameter (CP) settings is not further studied, and the method to determine the values of CPs is more likely to use the trial-and-error method, which lacks universality on both unimodal and multimodal benchmarks. To completely account for the effect of CPs in HDE, the Taguchi method is utilized to identify the values of each CP. The orthogonal experiment result is the average rank of the mean values of 23 benchmark functions obtained by HDE and other classic optimization algorithms. Based on the analysis of variance results, three CPs that have a major effect on the performance of HDE are selected, and each of them is changed from level 1 to level 5 to further obtain the best combination of CPs, which is indicated as HDEN1. To further enhance the local search ability of HDEN1 for the global best, a modified algorithm (HDEN2) is proposed based on a novel mutation strategy selection method, and the simulation results demonstrate that the minimum values obtained by HDEN2 are smaller than those obtained by HDEN1. Two EOPs, including planar microwave absorber and Yagi-Uda antenna designs, are solved to validate the performance of HDEN1 and HDEN2. The results reveal that the HDEN1 and HDEN2 outperform HDE, demonstrating the efficacy of the proposed method for identifying the CPs of HDE. In the end, a low profile and wideband RCS reduction pixelated checkboard metasurface is optimized utilizing the HDEN2, proving that the proposed algorithm can be a good candidate for hybrid real-binary electromagnetic problems.

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Section: Parameters Selectionmentioning

confidence: 99%