  In recent years several swarm optimization algorithms, such as Bat Algorithm
(BA) have emerged, which was proposed by Xin-She Yang in 2010. The idea of the
algorithm was taken from the echolocation ability of bats.
  Purpose: The purpose of this study is to provide the reader with a full study
of the Bat Algorithm, including its limitations, the fields that the algorithm
has been applied, versatile optimization problems in different domains, and all
the studies that assess its performance against other meta-heuristic
algorithms.
  Approach: Bat Algorithm is given in-depth in terms of backgrounds,
characteristics, limitations, it has also displayed the algorithms that
hybridized with BA (K-Medoids, Back-propagation neural network, Harmony Search
Algorithm, Differential Evaluation Strategies, Enhanced Particle Swarm
Optimization, and Cuckoo Search Algorithm) and their theoretical results, as
well as to the modifications that have been performed of the algorithm
(Modified Bat Algorithm (MBA), Enhanced Bat Algorithm (EBA), Bat Algorithm with
Mutation (BAM), Uninhabited Combat Aerial Vehicle-Bat algorithm with Mutation
(UCAV-BAM), Nonlinear Optimization)...
  Findings: Shed light on the advantages and disadvantages of this algorithm
through all the researches that dealt with the algorithm in addition to the
fields and applications it has addressed in the hope that it will help
scientists understand and develop it.
  Originality/value: As far as the research community knowledge, there is no
comprehensive survey study conducted on this algorithm cover{\i}ng all its
aspects.
  Keywords: Swarm Intelligence; Nature-Inspired Algorithms; Metaheuristic
Algorithms; Optimization Algorithms; Bat Algorithm.
