Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm
To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy impr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/12543 |
| _version_ | 1797461734671253504 |
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| author | Zhongxin Wang Jian Qin Zijiang Hu Jian He Dong Tang |
| author_facet | Zhongxin Wang Jian Qin Zijiang Hu Jian He Dong Tang |
| author_sort | Zhongxin Wang |
| collection | DOAJ |
| description | To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy improved sparrow search algorithm (MISSA) to optimize a BP neural network. Three strategies, namely Bernoulli chaotic mapping, inertial weights, and t-distribution, are introduced into the sparrow search algorithm to improve its convergent speed and accuracy. Using the Bernoulli chaotic map to process the population of sparrows to enhance its population richness, the weight is introduced into the updated position of the sparrow to improve its search ability. The adaptive t-distribution is used to interfere and mutate some individual sparrows to make the algorithm reach the optimal solution more quickly. The initial parameters of the BP neural network were optimized using the improved sparrow search algorithm to obtain the optimized MISSA-BP antenna surrogate model. This model is combined with multi-objective particle swarm optimization (MOPSO) to solve the design problem of the multi-objective antenna and verified by a triple-frequency antenna. The simulated results show that this method can predict the performance of the antennas more accurately and can also design the multi-objective antenna that meets the requirements. The practicality of the method is further verified by producing a real antenna. |
| first_indexed | 2024-03-09T17:23:33Z |
| format | Article |
| id | doaj.art-eb6a35c906b74c9c8be93ec1164040af |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:23:33Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-eb6a35c906b74c9c8be93ec1164040af2023-11-24T13:00:26ZengMDPI AGApplied Sciences2076-34172022-12-0112241254310.3390/app122412543Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search AlgorithmZhongxin Wang0Jian Qin1Zijiang Hu2Jian He3Dong Tang4School of Electronic and Communication Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 510006, ChinaSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 510006, ChinaTo solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy improved sparrow search algorithm (MISSA) to optimize a BP neural network. Three strategies, namely Bernoulli chaotic mapping, inertial weights, and t-distribution, are introduced into the sparrow search algorithm to improve its convergent speed and accuracy. Using the Bernoulli chaotic map to process the population of sparrows to enhance its population richness, the weight is introduced into the updated position of the sparrow to improve its search ability. The adaptive t-distribution is used to interfere and mutate some individual sparrows to make the algorithm reach the optimal solution more quickly. The initial parameters of the BP neural network were optimized using the improved sparrow search algorithm to obtain the optimized MISSA-BP antenna surrogate model. This model is combined with multi-objective particle swarm optimization (MOPSO) to solve the design problem of the multi-objective antenna and verified by a triple-frequency antenna. The simulated results show that this method can predict the performance of the antennas more accurately and can also design the multi-objective antenna that meets the requirements. The practicality of the method is further verified by producing a real antenna.https://www.mdpi.com/2076-3417/12/24/12543antenna designsurrogate modelimproved sparrow search algorithmmulti-objective antennaprediction of performance |
| spellingShingle | Zhongxin Wang Jian Qin Zijiang Hu Jian He Dong Tang Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm Applied Sciences antenna design surrogate model improved sparrow search algorithm multi-objective antenna prediction of performance |
| title | Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm |
| title_full | Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm |
| title_fullStr | Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm |
| title_full_unstemmed | Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm |
| title_short | Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm |
| title_sort | multi objective antenna design based on bp neural network surrogate model optimized by improved sparrow search algorithm |
| topic | antenna design surrogate model improved sparrow search algorithm multi-objective antenna prediction of performance |
| url | https://www.mdpi.com/2076-3417/12/24/12543 |
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