Bat-Inspired Algorithm and

it’sApplicationsMd.Al_Imran Roton

University of Dhaka Bangladesh

INTRODUCTIONBAT 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 APPLICATIONSPROS and CONS SUMMARY REFERANCES

Outline

Meta-heuristic algorithms such as particle swarm optimization, firefly algorithm andharmony search are now becoming powerful methods for solving many tough optimization problems.

Introduction

ANALYSIS NUMERICAL METHODS

VERIFICATIONVALIDATION

SENSITIVITY ANALYSIS

REALWORLD

PROBLEM

ALGORITHM,MODEL,

SOLUTION TECHNIQUE

COMPUTER IMPLIMENTATION

OPTIMIZATION PROCESS

The vast majority of heuristic and meta-heuristic algorithms have been derivedfrom the behavior of biological systems and/or physical systems in nature.

The Bat Algorithm (BA), based on the echolocation behavior of bats.

Count…..

Bat-inspired algorithm is a meta-heuristic 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 Algorithm

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.

BEHAVIOUR OF MICROBATS

Bat send sound signal with frequency f

Echo signal used to calculate the distance S

ACOUSTICS OF ECHOLOCATIONPULSE DURATION8 to 10 ms ULTRASONIC BURST DURATION

5 to 20 ms FREQUENCY RANGE25 kHz to 150 kHz

BURST RATE10 to 200 per second

PULSE110dB

3-D scenari

o

Time delay

between emission

and detection

Time difference between their two

ears

Loudness variations

of the echoes

Moving speed of Prey

Distance of Prey

Type & Orientation of Prey

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 f min, 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 A min.

IDEALIZED RULES OF BA

No ray tracing is used in estimating the time delay and 3 dimensional topology.

frequency f in a range [fmin, fmax]

In this paper used Frequency [20kHz to 500kHz]

wavelengths [λmin, λmax]

In this paper used Wavelength [0.7mm to 17mm]

SIMPLIFIED ASSUMPTIONS

Bat Motion

fi= fmin+ (fmax− fmin)β

vit+1= vi

t+ (xit-1–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

xnew= xold+ ЄAt

Є ∈ [−1,1]

At = <Ait> is the average loudness of

all the bats at this time step

Random Walk

Ait+1 = αAi

t, ri

t = ri0[1 − exp(−γt)],

Where α and γ are constants.

LOUDNESS AND PULSE EMISSION

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

PSEUDO CODE OF THE BAT ALGORITHM

FLOWCHART

VARIENTS 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)

Application

APPLICATIONS

Continuous Optimizatio

n in engineering

designCombinatorial Optimization

and Scheduling

Inverse Problems

and Parameter Estimatio

nClassifications, Clustering

and Data Mining

Image Processin

g

Fuzzy Logic and Other Application

s

Pros 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

PROS and CONS

◦ Cons 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.

PROS and CONS

Possible works for improve the algorithm :Parameter tuning.Parameter control.Speedup of coverage.Add Bat smell observation property.

Possible future works

Possible works for Apply the algorithm :Image segmentation and matching.Data clustering.Data classification.Path planning.Numerical optimization.Business optimization.Transport Engineering.Optimization in microelectronic application.

Possible future works

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.

Summary

1. Xin-She Yang, “A New Metaheuristic Bat-Inspired Algorithm”, NICSO 2010, SCI 284, pp. 65–74, 2010.

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. Xin-She Yang, “Bat algorithm: literature review and applications”, Int. J. Bio-Inspired Computation, Vol. 5, No. 3, pp. 141–149 (2013).

5. 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

6. 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

7. 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

8. Iztok Fister Jr., Duˇsan Fister, Xin-She Yang, “A Hybrid Bat Algorithm”, Elektrotehniški vestnik, 2013, in press

REFERANCES

9. Iztok Fister Jr., Duˇsan Fister, Xin-She Yang, “A Hybrid Bat Algorithm”, Elektrotehniški vestnik, 2013, in press

10. Du, Z. Y., Liu B., (2012). Image matching using a bat algorithm with mutation,Applied Mechanics and Materials, Vol. 203, No. 1, pp. 88–93.

11. Komarasamy, G., and Wahi, A., (2012). An optimized K-means clustering techniqueusing bat algorithm, European J. Scientific Research, Vol. 84, No. 2, pp.263-273.

12. Wang, G. G, Guo, L. H., Duan, H., Liu, L, Wang, H. Q., (2012).A bat algorithm with mutation for UCAV path planning, Scientific World Journal, Vol. 2012, 15 pages. doi:10.1100/2012/418946http://www.hindawi.com/journals/tswj/2012/418946/

13. Wang, Gaige, and Guo, Lihong, (2013). A novel hybrid bat algorithm with harmony search for global numerical optimization, Journal of Applied Mathematics,(in press).

14. Yang, X. S., Deb, S., and Fong, S., (2011). Accelerated particle swarm optimization and support vector machine for business optimization and applications, in:Networked Digital Technologies 2011, Communications in Computer and Information Science, 136, pp. 53–66.

15. Yang, X. S., Gandomi, A. H., Talatahari, S., Alavi, A. H., (2012a). Metaheuristicsin Water, Geotechnical and Transport Engineering, Elsevier, London, UK andWaltham, USA.

16. Yang, X. S., Karamanoglu, M., Fong, S., (2012b). Bat aglorithm for topologyoptimization in microelectronic applications, in: IEEE Int. Conference on FutureGeneration Communication Technology (FGCT2012), British Computer Society,12-14 Dec 2012, London, pp. 150–155.

16 Zhang, J. W., and Wang, G. G., (2012). Image matching using a bat algorithmwith mutation, Applied Mechanics and Materials (Editted by Z. Y. Du and Bin

REFERANCES

Thank You