  Extracting the latent information in high-dimensional and incomplete matrices
is an important and challenging issue. The Latent Factor Analysis (LFA) model
can well handle the high-dimensional matrices analysis. Recently, Particle
Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the
hyper-parameters adaptively with high efficiency. However, the incorporation of
PSO causes the premature problem. To address this issue, we propose a
sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which
refines the latent factors obtained by the PSO-incorporated LFA model. The
A2BAS algorithm consists of two sub-algorithms. First, we design an improved
BAS algorithm which adjusts beetles' antennae and step-size with Adam; Second,
we implement the improved BAS algorithm to optimize all the row and column
latent factors sequentially. With experimental results on two real
high-dimensional matrices, we demonstrate that our algorithm can effectively
solve the premature convergence issue.
