  Nature-inspired algorithms are among the most powerful algorithms for
optimization. In this study, a new nature-inspired metaheuristic optimization
algorithm, called bat algorithm (BA), is introduced for solving engineering
optimization tasks. The proposed BA is based on the echolocation behavior of
bats. After a detailed formulation and explanation of its implementation, BA is
verified using eight nonlinear engineering optimization problems reported in
the specialized literature. BA has been carefully implemented and carried out
optimization for eight well-known optimization tasks. Then, a comparison has
been made between the proposed algorithm and other existing algorithms. The
optimal solutions obtained by the proposed algorithm are better than the best
solutions obtained by the existing methods. The unique search features used in
BA are analyzed, and their implications for future research are also discussed
in detail.
