Fast multi-objective optimization of multi-parameter antenna structures based on improved MOEA/D with surrogate-assisted model
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- 作者：Jian Dong<sup>asup><sup> ; sup><sup>dongjian@csu.edu.cnsup> ; Qianqian Li<sup>asup><sup> ; sup><sup>294126260@qq.comsup> ; Lianwen Deng<sup>bsup><sup> ; sup><sup>dlw626@163.comsup>
- 关键词：Multi-objective optimization ; Antenna design ; MOEA/D ; Surrogate model
- 刊名：AEU - International Journal of Electronics and Communications
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：72
- 期：Complete
- 页码：192-199
- 全文大小：573 K
- 卷排序：72
文摘
For multi-objective design of multi-parameter antenna structures, optimization efficiency and computational cost are two major concerns. In this paper, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to improve global optimization capability by diversity detection operation and mixed population update operation. Further, in order to reduce the computational cost, a hybrid optimization strategy integrating a dynamically updatable surrogate-assisted model into the improved MOEA/D is proposed. The numerical results of test functions show that our algorithm outperforms original MOEA/D, modified MOEA/D (M-MOEA/D), and nondominated sorting genetic algorithm II (NGSA-II) in terms of diversity. Experimental validation of Pareto-optimal planar miniaturized multiband antenna designs is also provided, showing excellent convergence and considerable computational savings compared to those previously published approaches.