Bat Algorithm: A Survey of the State-of-the-Art

Abstract

The area of metaheuristic optimization algorithms has been attracting researchers for many years. These algorithms have built in capability to explore a large region of the solution space, are computationally robust, efficient and can avoid premature convergence. They have been extensively tested and applied on many hard optimization problems where conventional computing techniques perform unsatisfactorily. They are capable of solving general N-dimensional, linear, nonlinear and complex global optimization problems. One of the latest entrants in this field is the Bat algorithm which is based on the echolocation behaviour of bats. It has been proven to have good convergence properties on different benchmark functions and seems promising for dealing with optimization problems. The aim of this paper is to provide a survey of the state of the art on Bat algorithm. A concise effort has been made so that the readers get a rapid insight into some of the applications upon which bat algorithm has been applied till date in specialized fields of science and engineering. Some of the variants of the bat algorithm as reported in the literature have also been discussed.

A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem

“…• Bat Algorithm: BA [78] is based on the echolocation capability of microbats, which are able to find their prey and discriminate different kinds of insects even in complete darkness. Similarly to FA, BA is also a successful meta-heuristic, which has scored a promising performance in a variety of optimization problems [80,12].…”

Comparison of a Bat and Genetic Algorithm Generated Sequence Against Lead Through Programming When Assembling a PCB Using a Six-Axis Robot With Multiple Motions and Speeds

“…In BA, parameter control can be done in such a manner that the values of the parameters that include the loudness and rate of pulse emission can be varied as the iterations proceed. In this way, the BA provides inbuilt mechanism to automatically move from the exploration stage to exploitation stage when the optimal solution is approaching [36]. Frequency probability was set up in a way that assist the global search in early stage of the iterations.…”

A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

“…Bat algorithm (BA) was first developed by Yang for global optimization problems [60][61][62]. It was inspired by the echolocation behavior of microbats, with varying pulse rates of emission and loudness.…”