This article provides a comprehensive survey of the convergence analysis and applications of the Beetle Antennae Search (BAS) algorithm. BAS, inspired by the foraging behavior of beetles, is a heuristic optimization algorithm that simulates the beetles' use of antennae to detect odors and locate food sources. The review covers the basic principles, model structure, and differences from other heuristic algorithms. It also summarizes recent advancements in improving the BAS algorithm, including parameter adjustments, adaptive mechanisms, and hybrid approaches. The application of BAS in various fields such as function optimization, engineering design, and path planning is explored. The article concludes with future research directions, emphasizing deep learning fusion and multimodal problem processing. The review aims to provide a comprehensive understanding of the BAS algorithm's research status and prospects, offering valuable guidance for practical applications.This article provides a comprehensive survey of the convergence analysis and applications of the Beetle Antennae Search (BAS) algorithm. BAS, inspired by the foraging behavior of beetles, is a heuristic optimization algorithm that simulates the beetles' use of antennae to detect odors and locate food sources. The review covers the basic principles, model structure, and differences from other heuristic algorithms. It also summarizes recent advancements in improving the BAS algorithm, including parameter adjustments, adaptive mechanisms, and hybrid approaches. The application of BAS in various fields such as function optimization, engineering design, and path planning is explored. The article concludes with future research directions, emphasizing deep learning fusion and multimodal problem processing. The review aims to provide a comprehensive understanding of the BAS algorithm's research status and prospects, offering valuable guidance for practical applications.