An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques
In recent years, various methods from the evolutionary computation (EC) field have been applied to electromagnetic (EM) design problems and have shown promising results. However, due to the high computational cost of the EM simulations, the efficiency of directly using evolutionary algorithms is oft...
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| Vydané v: | IEEE transactions on antennas and propagation Ročník 62; číslo 1; s. 7 - 18 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York, NY
IEEE
01.01.2014
Institute of Electrical and Electronics Engineers |
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| ISSN: | 0018-926X, 1558-2221 |
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| Abstract | In recent years, various methods from the evolutionary computation (EC) field have been applied to electromagnetic (EM) design problems and have shown promising results. However, due to the high computational cost of the EM simulations, the efficiency of directly using evolutionary algorithms is often very low (e.g., several weeks' optimization time), which limits the application of these methods for many industrial applications. To address this problem, a new method, called surrogate model assisted differential evolution for antenna synthesis (SADEA), is presented in this paper. The key ideas are: (1) A Gaussian Process (GP) surrogate model is constructed on-line to predict the performances of the candidate designs, saving a lot of computationally expensive EM simulations. (2) A novel surrogate model-aware evolutionary search mechanism is proposed, directing effective global search even when a traditional high-quality surrogate model is not available. Three complex antennas and two mathematical benchmark problems are selected as examples. Compared with the widely used differential evolution and particle swarm optimization, SADEA can obtain comparable results, but achieves a 3 to 7 times speed enhancement for antenna design optimization. |
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| AbstractList | In recent years, various methods from the evolutionary computation (EC) field have been applied to electromagnetic (EM) design problems and have shown promising results. However, due to the high computational cost of the EM simulations, the efficiency of directly using evolutionary algorithms is often very low (e.g., several weeks' optimization time), which limits the application of these methods for many industrial applications. To address this problem, a new method, called surrogate model assisted differential evolution for antenna synthesis (SADEA), is presented in this paper. The key ideas are: (1) A Gaussian Process (GP) surrogate model is constructed on-line to predict the performances of the candidate designs, saving a lot of computationally expensive EM simulations. (2) A novel surrogate model-aware evolutionary search mechanism is proposed, directing effective global search even when a traditional high-quality surrogate model is not available. Three complex antennas and two mathematical benchmark problems are selected as examples. Compared with the widely used differential evolution and particle swarm optimization, SADEA can obtain comparable results, but achieves a 3 to 7 times speed enhancement for antenna design optimization. |
| Author | Excell, Peter Aliakbarian, Hadi Gielen, Georges Vandenbosch, Guy A. E. Bo Liu Zhongkun Ma |
| Author_xml | – sequence: 1 surname: Bo Liu fullname: Bo Liu email: b.liu@glyndwr.ac.uk organization: Dept. of Comput., Glyndwr Univ., Wrexham, UK – sequence: 2 givenname: Hadi surname: Aliakbarian fullname: Aliakbarian, Hadi email: Hadi.Aliakbarian@esat.kuleuven.be organization: ESAT, Katholieke Univ. Leuven, Leuven, Belgium – sequence: 3 surname: Zhongkun Ma fullname: Zhongkun Ma email: Zhongkun.Ma@esat.kuleuven.be organization: ESAT, Katholieke Univ. Leuven, Leuven, Belgium – sequence: 4 givenname: Guy A. E. surname: Vandenbosch fullname: Vandenbosch, Guy A. E. email: Guy.Vandenbosch@esat.kuleuven.be organization: ESAT, Katholieke Univ. Leuven, Leuven, Belgium – sequence: 5 givenname: Georges surname: Gielen fullname: Gielen, Georges email: Georges.Gielen@esat.kuleuven.be organization: ESAT, Katholieke Univ. Leuven, Leuven, Belgium – sequence: 6 givenname: Peter surname: Excell fullname: Excell, Peter email: p.excell@glyndwr.ac.uk organization: Inst. of Arts, Sci. & Technol., Glyndwr Univ., Wrexham, UK |
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| Keywords | Performance evaluation Electromagnetism Evolutionary algorithm Industrial application Evolutionary computation Global optimum Antenna design optimization Computational complexity Particle swarm optimization efficient global optimization differential evolution Gaussian process Biomimetics Simulation surrogate model assisted evolutionary algorithm On line processing Artificial intelligence expensive black-box optimization Antenna Antenna synthesis |
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| SubjectTerms | Antenna design optimization antenna synthesis Antennas Applied classical electromagnetism Applied sciences Computational modeling differential evolution efficient global optimization Electromagnetic wave propagation, radiowave propagation Electromagnetism; electron and ion optics Exact sciences and technology expensive black-box optimization Fundamental areas of phenomenology (including applications) Gaussian process Mathematical model Optimization Physics Predictive models Radiocommunications surrogate model assisted evolutionary algorithm Telecommunications Telecommunications and information theory Training data |
| Title | An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques |
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