  One popular example of metaheuristic algorithms from the swarm intelligence
family is the Bat algorithm (BA). The algorithm was first presented in 2010 by
Yang and quickly demonstrated its efficiency in comparison with other common
algorithms. The BA is based on echolocation in bats. The BA uses automatic
zooming to strike a balance between exploration and exploitation by imitating
the deviations of the bat's pulse emission rate and loudness as it searches for
prey. The BA maintains solution diversity using the frequency-tuning technique.
In this way, the BA can quickly and efficiently switch from exploration to
exploitation. Therefore, it becomes an efficient optimizer for any application
when a quick solution is needed. In this paper, an improvement on the original
BA has been made to speed up convergence and make the method more practical for
large applications. To conduct a comprehensive comparative analysis between the
original BA, the modified BA proposed in this paper, and other state-of-the-art
bio-inspired metaheuristics, the performance of both approaches is evaluated on
a standard set of 23 (unimodal, multimodal, and fixed-dimension multimodal)
benchmark functions. Afterward, the modified BA was applied to solve a
real-world job scheduling problem in hotels and restaurants. Based on the
achieved performance metrics, the proposed MBA establishes better global search
ability and convergence than the original BA and other approaches.
