The document summarizes the bat algorithm (BA), a swarm intelligence technique inspired by bat echolocation behavior. It describes how BA mimics how real bats use echolocation to locate prey, varying the loudness and rate of their ultrasonic calls. The basic steps of the BA are outlined, including initializing a population of solutions, adjusting their position and velocity based on the best solutions, and updating parameters like loudness and pulse rate between iterations. Applications of BA include optimization problems in areas like engineering, scheduling, data mining, and image processing.

1. Company
LOGO
Scientific Research Group in Egypt (SRGE)
Swarm Intelligence (5)
Bat Algorithm (BA)
Dr. Ahmed Fouad Ali
Suez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt

2. Company
LOGO Scientific Research Group in Egypt
www.egyptscience.net

3. Company
LOGO Outline
1.Bat algorithm (BA) (History and main idea)
4. The basic steps of the Bat Algorithm
3. Characteristics of microbats
5. Application of the Bat Algorithm
2. Echolocation of microbats
6. References

4. Company
LOGO Bat algorithm (BA) (History and main idea)
• Bat algorithm (BA) is a bio-inspired
algorithm developed by Yang in 2010.
• BA uses a frequency-tuning technique
to increase the diversity of the
solutions in the population.
• BA uses the automatic zooming to try
to balance exploration and exploitation
during the search process by mimicking
the variations of pulse emission rates
and loudness of bats when searching
for prey.

5. Company
LOGO Echolocation of microbats
• There are about 1000 different species
of bats.
• Their sizes can vary widely, ranging
from the tiny bumblebee bat of about
1.5 to 2 grams to the giant bats with
wingspan of about 2 m and may weight
up to about 1 kg.
• Microbats use echolocation extensively,
to a certain degree, while megabats do
not.

6. Company
LOGO Echolocation of microbats (Cont.)
• Microbats typically use a type of sonar,
called, echolocation, to detect prey, avoid
obstacles, and locate their roosting
crevices in the dark.
• They can emit a very loud sound pulse
and listen for the echo that bounces back
from the surrounding objects.
• Their pulses vary in properties and can be
correlated with their hunting strategies,
depending on the species.

7. Company
LOGO Characteristics of microbats
• All bats use echolocation to sense distance,
and they also know the difference between
food/prey and background barriers in some
magical way
• Bats fly randomly with velocity vi at
position xi with a frequency fmin, varying
wavelength and loudness A0 to search for
prey.
• They can automatically adjust the
wavelength (or frequency) of their emitted
pulses and adjust the rate of pulse emission
r ϵ [0, 1], depending on the proximity of their
target

8. Company
LOGO The basic steps of the Bat Algorithm

9. Company
LOGO The basic steps of the Bat Algorithm (Cont.)
• Step 1. The algorithm starts by setting
the initial values of its parameters and
the main iteration counter is set to zero
(lines 1-2).
• Step 2. The initial population is
generated randomly by generating the
initial position x0 and the initial
velocity v0 for each bat (solution) in
the population, the initial frequency fi
is assigned to each solution in the
population.

10. Company
LOGO The basic steps of the Bat Algorithm (Cont.)
• The initial population is evaluated by
calculating the objective function for
each solution in the initial population
f(xi
0) and the values of pulse rate ri and
loudness Ai is initialized (lines 3-9).
• The new population is generated by
adjusting the position xi and the
velocity vi for each solution in the
population as shown in Equations 6, 7,
8 (lines 12-13)

11. Company
LOGO The basic steps of the Bat Algorithm (Cont.)
where β ϵ [0, 1] is a random vector drawn from a uniform
distribution.

12. Company
LOGO The basic steps of the Bat Algorithm (Cont.)
• Step 4. The new population is
evaluated by calculating the objective
function for each solution and the best
solution x selected from the population
(lines 14-15).
• Step 5. The local search method is
applied in order to refine the best
found solution at each iteration (lines
16-19).

13. Company
LOGO The basic steps of the Bat Algorithm (Cont.)
• Step 6. The new solution is generated
randomly and accepted with some
proximity depending on parameter Ai,
the rate of pulse emission increases
and the loudness decreases.
• The values of Ai and ri are updated as
shown in Equations 9 and 10.
where α and γ are constant, the α parameter plays a similar
role as the cooling factor in the simulated annealing algorithm
(lines 21-24)

14. Company
LOGO The basic steps of the Bat Algorithm (Cont.)
Step 7. The new population is evaluated
and the best solution is selected from the
population.
• The operations are repeated until
termination criteria satisfied and the
overall solution is produced (lines 25-28)

15. Company
LOGO Application of the Bat Algorithm
• Continuous Optimization.
• Combinatorial Optimization and
Scheduling.
• Inverse Problems and Parameter
Estimation Classifications, Clustering
and Data Mining.
•Image Processing.
•Fuzzy Logic and Other Applications

16. Company
LOGO References
• Yang, X. S. and Gandomi, A. H., (2012). Bat algorithm: a
novel approach for global engineering optimization,
Engineering Computations, Vol. 29, No. 5, pp. 464–483.
•Xin-She Yang, Bat algorithm: literature review and
•applications, Int. J. Bio-Inspired Computation, Vol. 5, No. 3,
pp. 141–149 (2013).

17. Company
LOGO
Thank you
http://www.egyptscience.net
Ahmed_fouad@ci.suez.edu.eg