An Efficient Adaptive Improved-bat Algorithm

Aiming at the existence of basic bat algorithm (BA) optimization accuracy is not high, traps into local optima easily. This paper presents a new improved bat algorithm, which is named YSBA. In this algorithm, firstly, to simplify the calculation and improve the convergence speed, a new search equation is proposed in generate new solutions. Secondly, location constrict factor is added, which can be used to control with the location of the bats, balance the global and local search of bats and improve the optimization precision of the algorithm. Finally, reset the method of the calculation of loudness and rate, which can also be used to avoid trapping into local search. To verify the performance of our algorithm, 11 typical experiments are employed. The experimental results show that the new algorithm (YSBA) is significantly improved, which includes optimization accuracy, convergence speed, and they can also avoid falling into a local optimum.