  In order to solve the critical issues in Wireless Sensor Networks (WSNs),
with concern for limited sensor lifetime, nature-inspired algorithms are
emerging as a suitable method. Getting optimal network coverage is one of those
challenging issues that need to be examined critically before any network
setup. Optimal network coverage not only minimizes the consumption of limited
energy of battery-driven sensors but also reduce the sensing of redundant
information. In this paper, we focus on nature-inspired optimization algorithms
concerning the optimal coverage in WSNs. In the first half of the paper, we
have briefly discussed the taxonomy of the optimization algorithms along with
the problem domains in WSNs. In the second half of the paper, we have compared
the performance of two nature-inspired algorithms for getting optimal coverage
in WSNs. The first one is a combined Improved Genetic Algorithm and Binary Ant
Colony Algorithm (IGABACA), and the second one is Lion Optimization (LO). The
simulation results confirm that LO gives better network coverage, and the
convergence rate of LO is faster than that of IGA-BACA. Further, we observed
that the optimal coverage is achieved at a lesser number of generations in LO
as compared to IGA-BACA. This review will help researchers to explore the
applications in this field as well as beyond this area. Keywords: Optimal
Coverage, Bio-inspired Algorithm, Lion Optimization, WSNs.
