Conventional subspace, Compressive Sensing (CS) based methods are the estimation algorithms for off-grid direction of arrival which has the dictionary mismatch problem due to limited discretization of the grid points θ ∈ [−π/2 , −π/2]. These algorithms suffer high computational complexity and excessive overhead for accurate and need lot of time snapshots for proper DOA estimation. In this paper, we used combination of a particle swarm optimization and multiple signal classification (PSO-MUSIC) algorithm for direction-of-arrival (DOA) estimation for uniform linear array (ULA). The novel PSO-MUSIC and PSO-correlation algorithms present a systematic approach for searching the spatial spectrum peak, to find the accurate target position by updating the global best particle position iteratively. The statistical performance analysis of PSO-MUSIC and PSO-correlation algorithms shows that these method has better accuracy over other DOA estimation methods with single snapshot.