<i>K</i>-Means-Based Multigroup Differential Evolution Optimization Framework for Design of MIMO Antenna With Decoupling Elements
Zhen Zhang, Hongcai Chen, Fan Jiang, Yang Yu, Qingsha S. Cheng
Abstract
A novel differential evolution optimization method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means-based multigroup differential evolution (KMGDE) framework is proposed for multiple-input–multiple-output (MIMO) antenna design. The KMGDE algorithm divides the population into three groups using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means clustering method in each iteration. Different mutation strategies are assigned for each group according to its average fitness value. The groups are then combined for crossover operation with an adaptive crossover rate value to balance local search and global search. The fitness function considering the characteristics of the MIMO antenna is proposed to speed up optimization. The performance of the proposed method is evaluated and compared with six state-of-the-art optimization algorithms using an MIMO antenna example. The optimization results and the experimental results show that the proposed KMGDE algorithm has a faster convergence than state-of-the-art optimization algorithms, which significantly improves the efficiency of MIMO antenna design.