In this work, we proposed an adaptive beamformer based on a novel heuristic optimization algorithm. The novel optimization technique inspired from Fibonacci sequence principle, designated as Fibonacci branch search (FBS), used new tree's branches fundamental structure and interactive searching rules to obtain the global optimal solution in the search space. The branch structure of FBS is selected using two types of multidimensional points on the basis of shortening fraction formed by Fibonacci sequence; in this mode, interactive global and local searching rules are implemented alternately to obtain the optimal solutions, avoiding stagnating in local optimum. The proposed FBS is also used here to construct an adaptive beamforming (ABF) technique as a real-time implementation to achieve near-optimal performance for its simplicity and high convergence rate, then, the performance of the FBS is compared with the five typical heuristic optimization algorithms. Simulation results demonstrate the superiority of the proposed FBS approach in locating the optimal solution with higher precision and reveal further improvement in the ABF performance.

中文翻译：

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用于设计低旁瓣和深度归零自适应波束形成器的新型斐波那契分支搜索优化器的实现

在这项工作中，我们提出了一种基于新型启发式优化算法的自适应波束形成器。这种受斐波那契数列原理启发的新型优化技术，称为斐波那契分支搜索（Fibonacci branch search，FBS），利用新树的分支基本结构和交互搜索规则来获得搜索空间中的全局最优解。FBS的分支结构是在Fibonacci序列形成的缩短分数的基础上使用两类多维点选择的；在这种模式下，交互的全局和局部搜索规则交替执行以获得最优解，避免陷入局部最优。所提出的 FBS 还用于构建自适应波束成形 (ABF) 技术作为实时实现，因其简单性和高收敛速度而实现近乎最优的性能，然后将 FBS 的性能与五个典型的启发式算法进行比较优化算法。仿真结果证明了所提出的 FBS 方法在以更高的精度定位最优解方面的优越性，并揭示了 ABF 性能的进一步改进。