Three‐phase adaptive differential evolution for antenna array synthesis

Abstract

Antenna array synthesis is a complex nonlinear optimization problem. Differential evolution (DE) is an efficient method to solve the problem of antenna array synthesis. Different mutation strategies applied in different evolutionary phases can improve the search ability of DE. In this paper, a three‐phase adaptive differential evolution (TADE) algorithm is proposed for antenna array synthesis problems. In TADE, the evolution process is divided into three phases according to the population fitness distribution and iterative information, namely, the initial phase, the stable phase, and the precise phase. Based on statistical information of population fitness values, a suitable mutation strategy of each phase is designed for enhancing the optimization performance. The performance of the TADE algorithm is verified by a CEC 2013 test suite, three circular antenna array problems, and a planar antenna array problem. The results show that the TADE algorithm achieves better performance in solving antenna array synthesis than several advanced DE variants and some non‐DE global optimization algorithms.