Ensemble-Learning-Based Multiobjective Optimization for Antenna Design
Xinchen Wang, Gang Wang, Dong Wang, Qingfu Zhang
Abstract
An ensemble-learning-based multiobjective optimization is proposed for antenna design. By integrating the local search into multiobjective evolutionary algorithm based on decomposition (MOEA/D) and selecting appropriate solutions of the local search acquired with different offspring reproduction (OR) operators, the MOEA/D combined with ensemble OR (MOEA/D-EOR) is presented. Parallel local OR operators based on samples are also merged, for the first time, by exhaustively mining the evolution data of the optimization searching. The diversity and convergence of MOEA/D-EOR are verified by several widely used benchmark problems. The efficiency of MOEA/D-EOR is demonstrated by designing a high-performance bow-tie multiple-input and multiple-output (MIMO) antenna, which saves at least 25% of the optimization time. The overall performance of MOEA/D-EOR is further demonstrated by designing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 2 MIMO patch antenna in a compact size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.667\lambda _{0} \times 0.667\lambda _{0}$ </tex-math></inline-formula> , which achieves a high isolation of 24 dB in an operation band of 4.95–5.07 GHz.