Bat algorithm BA is a bio-inspired algorithm developed by Xin-She Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last three years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarised briefly here. In addition, we also discuss the essence of an algorithm and the links between algorithms and self-organisation. Further research topics are also discussed.

Bat algorithm: literature review and applications

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.

Bat Algorithm: Literature Review and Applications

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

https://downloads.hindawi.com/journals/tswj/2014/176718.pdf

Improved bat algorithm applied to multilevel image thresholding.

The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010 BA has been found to be very efficient As a result, the literature has expanded significantly since then This chapter provides a detailed introduction to BA and its new variants A wide range of diverse applications and case studies are also reviewed and summarized briefly

Chapter 10 – Bat Algorithms

Copper price estimation using bat algorithm

To solve large-scale optimization problems(OP),this paper constructed a bat algorithm with global convergence.In the algorithm,each bat was just an alternative solution of OP,used the principle of orthogonal Latin squares to construct initial positions of bat group so as to cover search space with balance dispersion and neat comparability,used the following behavior,autonomous behavior,averting-danger behavior and conformability behavior of bats to construct space positions trasnsfering strategies;used the loudness and rate of plus emission to ensure the bat groups keep either to stay unchanged or to transfer toward better positions,but never to transfer worse positions.During evoluation process,a bat's transferring from one position to another realized the search for the optimium solution.The result shows that the reducible stochastic matrix stability theorem can be applied to prove the global convergence of the algorithm.The case studys show that the algorithm has advantages of good suitability for different types of OPs and high convergence speed when applied to solve large-scale optimization problems.

LU Qiu-qin

Papers

Bat algorithm with global convergence for solving large-scale optimization problem