An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays. (3rd November 2020)

• Record Type: Journal Article
• Authors: Lin, Peng; Wang, Aimin; Zhang, Lin; Wu, Jing; Sun, Geng; Liu, Lingling; Lu, Lingfeng
• Is Part Of: International journal of numerical modelling. Volume 34:Number 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-03
• Subjects: antenna arrays -- cuckoo search -- invasive weed optimization -- reverse learning; Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2829
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: In order to overcome some shortcomings including the premature convergence and slow convergence speed at later evolution stage of conventional cuckoo search (CS) algorithm for the sidelobe suppressions of antenna arrays, an improved CS with reverse learning and invasive weed operators (ICSRLIWO) is proposed. First, ICSRLIWO algorithm generates reverse populations through opposition‐based learning method, then selects better individuals in the mixed population consists of the original and reversed population to form a high‐quality initial population. Second, ICSRLIWO introduces the reproducing, space diffusion and survival competition operators of invasive weed optimization to improve the population diversity and global search ability of conventional CS algorithm. Simulations are conducted based on 30 test functions in the CEC 2014 test suit to verify the performance of the proposed approach, and the results show that ICSRLIWO has higher solution accuracy and faster convergence speed than other comparison algorithms. In addition, ICSRLIWO also has advantages in solving the array antenna beam pattern optimization problems.