A Seminar I on

BAT OPTIMIZATION ALGORITHMBy

Ms. Harshada Anand GuravGuided by-

Dr. Kakandikar G.M 

Department of Mechanical EngineeringZeal Education Society’s

Dnyanganga College of Engineering and Research[2014-15]

CONTENT 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

OPTIMIZATION

PERFECT

FUNCTIONAL

EFFECTIVE

INTRODUCTION

OPTIMIZATION Engineering Optimization is the subject which

uses optimization techniques to achieve design goals in engineering.

PERFORMANCELIFETIME SERVICE

COSTMATERIAL USAGE

ANALYSIS NUMERICAL METHODS

VERIFICATIONVALIDATIONSESITIVITY ANALYSIS

REALWORLD

PROBLEM

ALGORITHM,MODEL,

SOLUTION TECHNIQUE

COMPUTER IMPLIMENTATION

OPTIMIZATION PROCESS

ALGORITHMS

OPTIMIZATION ALGORITHMS

DETERMINISTIC STOCHASTIC

HEURISTIC METAHEURISTIC

BAT ALGORITHM• 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.

BAEHAVIOUR OF MICROBATS

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

ACOUSTICS OF ECHOLOCATION

3-D scenari

o

Time delay between emission

and detection

Time difference between their two

ears

Loudness variations

of the echoes

Type & Orientation of Prey

Distance of Prey

Moving speed of Prey

IDEALIZED RULES OF BA 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]

Bat Motionfi= fmin+ (fmax−

fmin)β

vit+1= vi

t+ (xit–x*)fi

xit+1= xi

t+ vit

• β ∈ [0, 1]• fmin= 0 & fmax= 100

• x* is the current global best location

• t is number of iteration

RANDOM WALKxnew= xold+ ЄAt

Є ∈ [−1,1]At = <Ai

t> is the average loudness of all the bats at this time stepLOUDNESS AND PULSE EMISSIONAi

t+1 = αAit,

rit = 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 solutionsGenerate a local solution around the selected best solutionend ifGenerate a new solution by flying randomlyif(rand <Ai & f (xi) < f (x∗))

Accept the new solutionsIncrease ri and reduce Ai

end ifRank the bats and find the current best x∗

end whilePostprocess results and visualization

FLOWCHART

VARIANTS OF BAMulti-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 BAADVANTAGES 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

APPLICATIONS

Continuous Optimization

in engineering

designCombinatorial Optimization

and Scheduling

Inverse Problems

and Parameter Estimation

Classifications, Clustering and Data Mining

Image Processin

g

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.

REFERANCES1. 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