Bat Algorithm_Basics

A Seminar I on
BAT OPTIMIZATION ALGORITHM
By
Ms. Harshada Anand Gurav
Guided by-
Dr. Kakandikar G.M
Department of Mechanical Engineering
Zeal Education Society’s
Dnyanganga College of Engineering and Research
[2014-15]

INTRODUCTION
ALGORITHMS
BAT ALGORITHM
BEHAVIOUR OF MICROBATS
ACOUSTICS OF ECHOLOCATION
IDEALIZED RULES OF BA
BAT MOTION
LOUDNESS AND PULSE EMISSION
PSEUDO CODE OF THE BAT ALGORITHM
FLOWCHART
VARIENTS OF BA
ADVANTAGES AND DISADVANTAGES OF BA
APPLICATIONS
SUMMARY
REFERANCES

Engineering Optimization is the subject which
uses optimization techniques to achieve design
goals in engineering.
PERFORMANCE
LIFETIME SERVICE
COST
MATERIAL USAGE

ANALYSIS NUMERICAL METHODS
VERIFICATIONVALIDATION
SESITIVITY ANALYSIS
REAL
WORLD
PROBLEM
ALGORITHM,
MODEL,
SOLUTION
TECHNIQUE
COMPUTER
IMPLIMENTATION

OPTIMIZATION
ALGORITHMS
DETERMINISTIC STOCHASTIC
HEURISTIC METAHEURISTIC

• Bat-inspired algorithm is
a metaheuristic optimization algorithm developed by
Xin-She Yang in 2010. This bat algorithm is based on
the echolocation behaviour of micro bats with varying
pulse rates of emission and loudness.

Bat sends sound signal with
frequency f
Echo signal use to calculate
the distance S
Bats emit sonar signals in order to locate potential prey. This signals
bounce back if they hit an object. Bats are able to interpret the
signals to see if the object is large or small and if it is moving toward
or away from them.

PULSE DURATION
8 to 10 ms
ULTRASONIC BURST DURATION
5 to 20 ms
FREQUENCY RANGE
25 kHz to
150 kHz
BURST RATE
10 to 200
per second
PULSE
110dB
3-D
scenario
Time delay
between
emission
and
detection
Time
difference
between
their two
ears
Loudness
variations
of the
echoes

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
fixed frequency fmin, varying wavelength λ 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 ∈ [0,1],
depending on the proximity of their target.
Although the loudness can vary in many ways, we
assume that the loudness varies from a large (positive)
A0 to a minimum constant value Amin.

SIMPLIFIED ASSUMPTIONS
Frequency [20kHz to 500kHz]
Wavelength [0.7mm to 17mm]
fi= fmin+ (fmax− fmin)β
vi
t+1= vi
t+ (xi
t–x*)fi
xi
t+1= xi
t+ vi
t
• β ∈ [0, 1]
• fmin= 0 & fmax= 100
• x* is the current
global best location
• t is number of
iteration

RANDOM WALK
xnew= xold+ ЄAt
Є ∈ [−1,1]
At = <Ai
t> is the average loudness of all the bats at
this time step
LOUDNESS AND PULSE EMISSION
Ai
t+1 = αAi
t,
ri
t = ri
0[1 − exp(−γt)],
Where α and γ are constants.

PSEUDO CODE OF THE BAT ALGORITHM
Objective function f (x), x = (x1, ...,xd)T
Initialize the bat population xi (i = 1,2, ...,n) and vi
Define pulse frequency fi at xi
Initialize pulse rates ri and the loudness Ai
while(t <Max number of iterations)
Generate new solutions by adjusting frequency, and updating
velocities and locations/solutions
if ( rand > ri )
Select a solution among the best solutions
Generate a local solution around the selected best solution
end if
Generate a new solution by flying randomly
if(rand <Ai & f (xi) < f (x∗))
Accept the new solutions
Increase ri and reduce Ai
end if
Rank the bats and find the current best x∗
end while
Postprocess results and visualization

Multi-objective bat algorithm (MOBA) by Yang (2011)
Fuzzy Logic Bat Algorithm (FLBA) by Khan et al. (2011)
K-Means Bat Algorithm (KMBA) by Komarasamy and Wahi
(2012)
Chaotic Bat Algorithm (CBA) by Lin et al. (2012)
Binary bat algorithm (BBA) by Nakamura et al. (2012)
Differential Operator and Levy flights Bat Algorithm (DLBA)by
Xie et al. (2013)
Improved bat algorithm (IBA) by Jamil et al. (2013)

ADVANTAGES AND DISADVANTAGES OF BA
ADVANTAGES OF BA:-
Simple, Flexible and Easy to implement.
Solve a wide range of problems and highly non linear
problems efficiently.
Provides very quick convergence at a very initial stage by
switching from exploration to exploitation.
The loudness and pulse emission rates essentially provide a
mechanism for automatic control and auto-zooming into
the region.
It gives promising optimal solutions.
Works well with complicated problems
DISADVANTAGES OF BA:-
If we allow the algorithm to switch to exploitation stage too
quickly by varying A and r too quickly, it may lead to
stagnation after some initial stage.

APPLICATIONS
Continuous
Optimization
in
engineering
design
Combinatorial
Optimization
and
Scheduling
Inverse
Problems
and
Parameter
Estimation
Classifications,
Clustering and
Data Mining
Image
Processing
Fuzzy Logic
and Other
Applications

SUMMARY
• In this report, the concept, classification and various
techniques of optimization with its process are
discussed. The standard bat algorithm, working
principle, variants and its application areas are
presented. The advantages and disadvantages are also
mentioned. This report also focuses on the importance
of using BA as its having wide number of applications,
advantages and having fewer drawbacks.

REFERANCES
1. John W. Chinneck, Practical Optimization: a Gentle Introduction
2. Xin-She Yang, “Nature-Inspired Metaheuristic Algorithms” (Second Edition), University of
Cambridge, United Kingdom
3. Xin-She Yang, Amir Hossein Gandomi,“Bat Algorithm: A Novel Approach for Global
Engineering Optimization”,Engineering Computations, Vol. 29, Issue 5, pp. 464--483 (2012).
4. A. Hanif Halim, I. Ismail, “Bio-Inspired Optimization Method: A Review”, International
Journal of Information Systems, Volume 1 July 30, pp. 12-17 (2014)
5. Xin-She Yang, “A New Metaheuristic Bat-Inspired Algorithm”, NICSO 2010, SCI 284, pp. 65–
74, 2010.
6. Xin-She Yang, “Bat algorithm: literature review and applications”, Int. J. Bio-Inspired
Computation, Vol. 5, No. 3, pp. 141–149 (2013).
7. Sashikala Mishra, Kailash Shaw, Debahuti Mishra, “A New Metaheuristic Bat Inspired
Classification Approach for Microarray Data”, Procedia Technology, vol.4 Feb 2012, pp. 802 –
806
8. Selim Yılmaza, Ecir U. Kücüksille, “A new modification approach on bat algorithm for solving
optimization problems”, Applied Soft Computing, Volume 28, March 2015, Pages 259–275

9. Aaron Ezgi DenizUlker, Sadik Ulker, “Microstrip coupler design using bat algorithm”,
International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 5, No. 1, January 2014,
pp. 127-133
10. S. Balasubramaniyan, T. S. Sivakumaran, “Optimal location of facts devices for power quality
issues using PSO and bat algorithm”, Journal of Theoretical and Applied Information Technology,
Vol. 64 No.1, 10th June 2014, pp. 148-157
11. Xin-She Yang, “Bat Algorithm for Multi-objective Optimization”, Int. J. Bio-Inspired
Computation, Vol. 3, No. 5, pp.267-274.
12. R. Y. M. Nakamura, L. A. M. Pereira, K. A. Costa, D. Rodrigues, J. P. Papa, X. S. Yang, “BBA:
A Binary Bat Algorithm for Feature Selection”, Graphics, Patterns and Images (SIBGRAPI), Aug.
2012, pp: 291-297
13. Jian Xie, Yongquan Zhou, Huan Chen, “A Novel Bat Algorithm Based on Differential Operator
and Lévy Flights Trajectory”, Computational Intelligence and Neuroscience, Volume 2013
14. Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi, Siamak Talatahari, “Bat algorithm
for constrained optimization tasks”, Neural Comput & Applic, July 2013, pp:1239–1255
15. Xin-She Yang, Suash Deb, Simon Fong, “Multiple-Valued Logic and Soft Computing”, 2014,
pp. 223-237
16. Iztok Fister Jr., Duˇsan Fister, Xin-She Yang, “A Hybrid Bat Algorithm”, Elektrotehniški
vestnik, 2013, in press

Bat Algorithm_Basics

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