Open AccessHybrid optimisation method of improved genetic algorithm and IFT for linear thinned array
Author(s) -
Wang Zheng,
Sun Yuze,
Yang Xiaopeng,
Li Shuai
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0296
Subject(s) - crossover , algorithm , convergence (economics) , rate of convergence , computer science , range (aeronautics) , population , genetic algorithm , mathematical optimization , mathematics , engineering , key (lock) , artificial intelligence , demography , computer security , aerospace engineering , sociology , economics , economic growth
The array thinning technique can greatly reduce the number of the array elements while keeping the performance of the array almost the same. However, the existing algorithms have slow convergence rates and are easy to fall into local optimum. To improve the optimisation performance, a hybrid method based on improved genetic algorithm (GA) and iterative Fourier transform (IFT) technique for linear thinned array is proposed in this study. The population is divided into improved GA group and IFT group according to the convergence of the population and different operations can be done paralleled to generate offspring in each iteration. In the improved GA processing, adaptive crossover rate and mutation rate are used. The mechanism that keeps the fill factor stable is removed for larger search range. The IFT processing is executed paralleled for fast convergence velocity. The proposed hybrid method can obtain the fast convergence velocity and avoid being trapped into the local optimum by the combination of the two approaches. Several examples are simulated to validate the performance of the proposed method.