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Scientific Research Group in Egypt (SRGE) Swarm Intelligence (5)Bat Algorithm (BA)
Dr. Ahmed Fouad AliSuez Canal University,
Dept. of Computer Science, Faculty of Computers and informaticsMember of the Scientific Research Group in Egypt
CompanyLOGO 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
CompanyLOGO 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 searchingfor prey.
CompanyLOGO 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.
CompanyLOGO 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.
CompanyLOGO 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
CompanyLOGO
The basic steps of the Bat Algorithm
CompanyLOGO
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 theinitial 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.
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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)
CompanyLOGO
The basic steps of the Bat Algorithm (Cont.)
where β ϵ [0, 1] is a random vector drawn from a uniform distribution.
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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).
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The basic steps of the Bat Algorithm (Cont.)
• Step 6. The new solution is generated randomly and accepted with someproximity 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)
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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)
CompanyLOGO 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
CompanyLOGO 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).
