Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4954148 | AEU - International Journal of Electronics and Communications | 2017 | 8 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Jian Dong, Qianqian Li, Lianwen Deng,