Sidelobe-level suppression for linear and circular antenna arrays via the cuckoo search–chicken swarm optimisation algorithm

• Published in: IET microwaves, antennas & propagation Vol. 11; no. 2; pp. 209 - 218
• Main Authors: Liang, Shuang, Feng, Tie, Sun, Geng
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 29.01.2017
• Subjects: Algorithms; Amplitudes; Antenna arrays; antenna radiation patterns; CAA; chaos theory; circular antenna array; Circularity; communication system; CSCSO; cuckoo search‐chicken swarm optimisation algorithm; Excitation; LAA; Levy flight weight coefficient; linear antenna array; linear antenna arrays; optimisation; Optimization; particle swarm optimisation algorithm; radiation pattern optimisation; reliability; search problems; Searching; sidelobe‐level suppression; SLL suppression; Swarm intelligence; chaos theory; cuckoo search-chicken swarm optimisation algorithm; CAA; Levy flight weight coefficient; particle swarm optimisation algorithm; communication system; SLL suppression; reliability; radiation pattern optimisation; sidelobe-level suppression; LAA; circular antenna array; linear antenna array; optimisation; antenna radiation patterns; CSCSO; search problems; linear antenna arrays
• ISSN: 1751-8725; 1751-8733
• DOI: 10.1049/iet-map.2016.0083

An antenna array application with high directivity and low sidelobe level (SLL) is an important technology that can enhance the reliability and validity of a communication system. In recent years, swarm intelligence optimisation algorithms have been widely used in the design of antenna arrays. In this study, a hybrid heuristic swarm intelligence optimisation algorithm called cuckoo search–chicken swarm optimisation (CSCSO) is proposed to optimise the excitation amplitude of a linear antenna array (LAA) and the excitation amplitude and spacing between the array elements of a circular antenna array (CAA). The maximum SLL will be obtained while the mainlobe width is fixed. CSCSO combines cuckoo search (CS) and chicken swarm optimisation (CSO) and thus, it will have the excellent global search capability of CS as well as introduce the hierarchy mechanism of CSO to improve algorithm precision. Chaos theory is utilised to determine the initial solution of the algorithm, while Levy flight weight coefficient is used to improve the convergence rate in CSCSO. Simulation results show that CSCSO has a better performance in terms of solution accuracy and convergence rate in the radiation pattern optimisation of LAA and CAA compared with the standard CSO, CS, and particle swarm optimisation algorithms.