  Coverage of interest points is one of the most critical issues in directional
sensor networks. However, considering the remote or inhospitable environment
and the limitation of the perspective of directional sensors, it is easy to
form perception blind after random deployment. The intension of our research is
to deal with the bound-constrained optimization problem of maximizing the
coverage of target points. A coverage enhancement strategy based on a discrete
army ant search optimizer (DAASO) is proposed to solve the above problem, which
is inspired by the biological habits of army ants. A set of experiments are
conducted using different sensor parameters. Experimental results verify the
effectiveness of the DAASO in coverage effect when compared to the existing
methods.
