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.

中文翻译：

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用于天线阵列合成的三相自适应差分演化

天线阵列合成是一个复杂的非线性优化问题。差分进化（DE）是解决天线阵列合成问题的有效方法。在不同的进化阶段应用不同的变异策略可以提高DE的搜索能力。在本文中，针对天线阵列合成问题提出了一种三相自适应差分进化（TADE）算法。在TADE中，进化过程根据种群适应度分布和迭代信息分为三个阶段，即初始阶段、稳定阶段和精确阶段。根据种群适应度值的统计信息，设计合适的各阶段变异策略以提高优化性能。TADE 算法的性能由 CEC 2013 测试套件验证，三个圆形天线阵列问题和一个平面天线阵列问题。结果表明，TADE 算法在解决天线阵列合成方面的性能优于几种先进的 DE 变体和一些非 DE 全局优化算法。