House Swallow Optimizer: A New Meta-Heuristic Algorithm and Its Application for DOA Estimation of Broadband Signal in Vector Hydrophone Array

A new meta-heuristic algorithm, termed the House Swallow Optimizer (HSO), is proposed. Inspired by the sophisticated behavioral repertoire of house swallows, this algorithm addresses the limitations of conventional optimization methods in solving complex, high-dimensional problems. The HSO systematically simulates 6 key survival strategies observed in house swallows—roosting, hovering, preying, nesting, breeding and seasonal migration—through a 5-phase mathematical model to achieve effective exploration-exploitation balance. To evaluate the algorithm’s performance, we conducted comparative studies using 23 standardized benchmark functions against 5 established meta-heuristics: BKA, SCA, SOA, DE, and PSO. The experimental outcomes prove that HSO has a strong ability to balance exploration and development, with out-performance over comparative algorithms. In order to verify the engineering application potential of the optimization algorithm proposed in this paper, aiming at the problems of large estimation error, high computational complexity and low accuracy in the DOA estimation of broadband signal of vector hydrophone array, the HSO algorithm is applied to the ML algorithm, and the DOA estimation of broadband signal is successfully carried out. Initially, the SST algorithm is employed for signal preprocessing, where broadband signals are transformed into narrowband equivalents through eigenvalue decomposition. This step facilitates the formulation of a ML-based DOA estimation model, which is subsequently optimized using the HSO algorithm. Simulation results demonstrate that the proposed HSO-ML method is superior to the ML implementation of other algorithms. Specifically, our approach achieves faster convergence speed, lower root mean square error, lower computational complexity and more robust performance.