  The efficiency of any metaheuristic algorithm largely depends on the way of
balancing local intensive exploitation and global diverse exploration. Studies
show that bat algorithm can provide a good balance between these two key
components with superior efficiency. In this paper, we first review some
commonly used metaheuristic algorithms, and then compare the performance of bat
algorithm with the so-called intermittent search strategy. From simulations, we
found that bat algorithm is better than the optimal intermittent search
strategy. We also analyse the comparison results and their implications for
higher dimensional optimization problems. In addition, we also apply bat
algorithm in solving business optimization and engineering design problems.
