Sparse Array Optimization Based on Bit-by-Bit Search Algorithm

This paper presents a sparse array optimization method based on the bit-by-bit search strategy, specifically aimed at reducing the peak side-lobe level of the array. The algorithm initiates with a random global exploration, followed by a localized refinement through sequential bit-wise optimization, ingeniously integrating greedy strategies with principles of dynamic programming. This approach effectively circumvents local optima while accelerating convergence to the globally optimal configuration. Experimental results under comparable conditions demonstrate that, compared to existing methods such as Genetic Algorithms and Artificial Bee Colony algorithms, this strategy achieves significant enhancements in array performance while concurrently reducing computational complexity.