A modified ant lion optimization (MALO) algorithm is proposed in this article, for the synthesis of Chebyshev-based arrays by optimizing amplitudes and phases of excitations, and element spacings. Modification in ant lion optimization is achieved by hybridizing it with chaotic particle swarm optimization. The optimization process is employed to obtain an array pattern with the least possible sidelobe level. Close-in sidelobe level minimization for optimum pattern synthesis is suggested. Instead of only steering the main beam towards the desired direction presented by some popular optimization methods, the beam steering along with null positioning in other specified direction is also achieved employing MALO. Considering the arrays with the same design parameters and the results of other optimization algorithms, the performance of MALO is evaluated. The results show that MALO provides considerable improvements in an array pattern compared to the arrays optimized using other optimization algorithms and the uniform array.

中文翻译：

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使用改进的蚁狮优化的基于切比雪夫的波束转向和零位定位阵列

本文提出了一种改进的蚁狮优化 (MALO) 算法，用于通过优化激励的幅度和相位以及元素间距来合成基于 Chebyshev 的阵列。蚁狮优化的修改是通过将其与混沌粒子群优化混合来实现的。采用优化过程来获得具有最小可能旁瓣电平的阵列图案。建议对最佳模式合成进行近旁旁瓣电平最小化。与一些流行的优化方法仅将主光束转向所需方向不同，使用 MALO 还可以实现光束转向以及在其他指定方向上的零位定位。考虑具有相同设计参数的阵列和其他优化算法的结果，评估MALO的性能。