Self Adaptive Improved Bat Algorithm

EasyChair Preprint no. 1859

Abstract

Collective intelligence is one of the strongest optimization techniques which act based on the group behavior of the creatures. The bat algorithm which was proposed by yang in 2010 is an algorithm inspired by the collective behavior of bats in the natural environment. Since then, researchers have been trying to improve the bat algorithm continuously. This paper attempts to solve simple and complex mathematical equations by improving the performance of the bat algorithm. Therefore, to improve the performance of the algorithm, it modifies the speed and motion relationships of the bats so that the bats move is considered as a way of optimizing the solutions to the target in an adaptive manner. In addition, to get rid of the local optimom, mutation operator is employed to check all search space points. The results show the superiority of the corrected algorithm over other existing ones.

Keyphrases: Bat Algorithm, collective intelligence, Optimization Techniques

@Booklet{EasyChair:1859,
  author = {Jalil Alahloei Zare and Behroz Keshtegar},
  title = {Self Adaptive Improved Bat Algorithm},
  howpublished = {EasyChair Preprint no. 1859},

  year = {EasyChair, 2019}}