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Bat-Inspired Algorithm and
it’sApplicationsMd.Al_Imran Roton
University of Dhaka Bangladesh
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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
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Meta-heuristic algorithms such as particle swarm optimization, firefly algorithm andharmony search are now becoming powerful methods for solving many tough optimization problems.
Introduction
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ANALYSIS NUMERICAL METHODS
VERIFICATIONVALIDATION
SENSITIVITY ANALYSIS
REALWORLD
PROBLEM
ALGORITHM,MODEL,
SOLUTION TECHNIQUE
COMPUTER IMPLIMENTATION
OPTIMIZATION PROCESS
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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…..
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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
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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
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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
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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
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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
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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
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xnew= xold+ ЄAt
Є ∈ [−1,1]
At = <Ait> is the average loudness of
all the bats at this time step
Random Walk
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Ait+1 = αAi
t, ri
t = ri0[1 − exp(−γt)],
Where α and γ are constants.
LOUDNESS AND PULSE EMISSION
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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
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FLOWCHART
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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)
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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
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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
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◦ 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
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Possible works for improve the algorithm :Parameter tuning.Parameter control.Speedup of coverage.Add Bat smell observation property.
Possible future works
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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
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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
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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
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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
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Thank You