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An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays
Author(s) -
Lin Peng,
Wang Aimin,
Zhang Lin,
Wu Jing,
Sun Geng,
Liu Lingling,
Lu Lingfeng
Publication year - 2020
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2829
Subject(s) - cuckoo search , population , premature convergence , computer science , convergence (economics) , mathematical optimization , algorithm , antenna (radio) , particle swarm optimization , mathematics , telecommunications , demography , sociology , economics , economic growth
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.