Bat algorithm is metaheuristic that can be applied for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness
Bat algorithm explained. slides ppt pptxMahdi Atawneh
[Important]
Some numbers in the example are not correct ( in iteration 3 and later), I used them to clarify the idea only.
For people who asked me about the random number that appears in the slide:
Overview:
As described in the paper and pseudo code.
We have two important variables ( ri,Ai) for each bat, these variables will be used to evaluate the bats( solutions).
When a bat becomes near the goal, “ri” value will be increased, and “Ai” will be decreased.
*** About the Random variable:
At each iteration,
- The algorithm will have the solutions population ( assume we have 10 bats ), these solutions(bats) values are near each other.
- To prevent the algorithm from falling at local minima, the algorithm at each iteration will generate a random solution (bat) to explore, this could in some cases jump to a new solution that is near the goal.
- So in the slides, the “rand” means the random solution. We will compare it to all other solutions. If the random solution “ri” value is the height we will put this bat in the best solutions array.
Bat algorithm
download the Powerpoint file pptx with animations
https://docs.google.com/presentation/d/0Bxij58M-C_RgY2gxOEFHSlZzWHM/edit?usp=sharing&ouid=117863559816378751483&resourcekey=0-94EJhpYOuJtlSGiJlRH3jQ&rtpof=true&sd=true
The original paper: https://www.researchgate.net/publication/45913690_A_New_Metaheuristic_Bat-Inspired_Algorithm
Bat algorithm explained. slides ppt pptxMahdi Atawneh
[Important]
Some numbers in the example are not correct ( in iteration 3 and later), I used them to clarify the idea only.
For people who asked me about the random number that appears in the slide:
Overview:
As described in the paper and pseudo code.
We have two important variables ( ri,Ai) for each bat, these variables will be used to evaluate the bats( solutions).
When a bat becomes near the goal, “ri” value will be increased, and “Ai” will be decreased.
*** About the Random variable:
At each iteration,
- The algorithm will have the solutions population ( assume we have 10 bats ), these solutions(bats) values are near each other.
- To prevent the algorithm from falling at local minima, the algorithm at each iteration will generate a random solution (bat) to explore, this could in some cases jump to a new solution that is near the goal.
- So in the slides, the “rand” means the random solution. We will compare it to all other solutions. If the random solution “ri” value is the height we will put this bat in the best solutions array.
Bat algorithm
download the Powerpoint file pptx with animations
https://docs.google.com/presentation/d/0Bxij58M-C_RgY2gxOEFHSlZzWHM/edit?usp=sharing&ouid=117863559816378751483&resourcekey=0-94EJhpYOuJtlSGiJlRH3jQ&rtpof=true&sd=true
The original paper: https://www.researchgate.net/publication/45913690_A_New_Metaheuristic_Bat-Inspired_Algorithm
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems.
Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems.
Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
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Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
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Ashraf El Zarka, VP and Managing Director MEA, UiPath
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Boris Krumrey, Global VP, Automation Innovation, UiPath
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3. • After hitting and reflecting, the bats transform their own pulse into
useful information to explore how far away the prey is.
• The pulse rate can be simply determined in the range from 0 to 1,
where 0 means that there is no emission and 1 means that the bat’s
emitting is their maximum.
4. • Bat sends signal with frequency f.
• Echo signal used to calculate the distance.
B
C1
C2
C3
B
C1
C2
C3
Frequency
Frequency
S1
S2
S3
5. IDEALIZED RULES FOR BAT ALGORITHM
• All bats use echolocation to sense distance and they know the
difference between food/prey.
• 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 of their emitted pulses
and adjust the rate of pulse emission r[0,1], depending on the
proximity of the target.
7. Algorithm: Bat Algorithm
• 1. Initialize Bat population: Xi (i = 1, 2, ..., n)
• 2. Define frequency Fi and velocity Vi
• 3. Initialize pulse rates ri and the loudness Ai
• 4. while t< Maximum iterations do
• 5. update frequency and velocity
• 6. Calculate transfer function values using Equation (4)
• 7. Update Vi, Xi, and Fi using Equations 5 to 7
• 8. if (rand >ri ) then
• 9. Select the global best solution (Gbest) among the available best solutions and with the available Gbest
dimensions modify the dimensions of Xi randomly.
• 10. end
• 11. Generate new solution randomly Equation (8)
• 12. if ((rand <Ai) and (F(Xi)<F(Gbest))then
• 13. Accept the new solutions increase ri and reduce Ai using Equations (9 to 10)
• 14. end
• 15. Find the current Gbest and Rank the best
• 16. end
8. MATHEMATICAL EQUATIONS
• Generating new solutions is performed by moving virtual bats
according to the following equations:
• 𝑓𝑖 = 𝑓 𝑚𝑖𝑛 + 𝑓𝑚𝑎𝑥 − 𝑓 𝑚𝑖𝑛 𝛽
• 𝑣𝑖
𝑡
= 𝑣𝑖
𝑡−1
+ (𝑥𝑖
𝑡
− 𝑥∗) 𝑓𝑖
• 𝑥𝑖
𝑡
= 𝑥𝑖
𝑡−1
+ 𝑣𝑖
𝑡
• Where βЄ[0,1] is a random vector drawn from a uniform distribution.
• X* is the current global best location (solution) which is located after
comparing all the solutions among all the bats.
9. MATHEMATICAL EQUATIONS
• The current best solution according to the equation:
• where δ Є[-1,1] is a random number, while At is the average loudness
of all the best at this time step.
𝑥 𝑛𝑒𝑤 = 𝑥 𝑜𝑙𝑑 +𝛿𝐴 𝑡
10. MATHEMATICAL EQUATIONS
• The loudness can be chosen as any value of convenience as the
loudness usually decreases once a bat has found its prey, while the rate
of pulse emission increases.
11. LOUDNESS AND PULSE EMISSION VS ITERARION
𝐴𝑖
𝑡+1
=∝ 𝐴𝑖
𝑡
𝑟𝑖
𝑡+1
= 𝑟𝑖
0
[1-exp(-𝛾t)]
13. EXAMPLE – SEGMENTATION
• The multilevel threshold problem can be configured as a k-
dimensional optimization problem for optimal thresholds [t1,t2,…tk]
which optimizes an objective function.
• The objective function is determined from the histogram of the image,
denoted as h(i), i = 0,1,2,…L-1, where h(i) represents the number of
pixels having the gray level i.
14. EXAMPLE – SEGMENTATION
• L gray levels in a given image I having M pixels and these gray levels
are in the range {0,1,…L-1}.
• The normalized probability at level i is defined by the ratio:
pi = h(i)/M
16. Algorithm 2: Binary Bat Algorithm
• 1. Initialize Bat population: Xi (i = 1, 2, ..., n) rand(0 or 1) and Vi= 0
• 2. Define pulse frequency Fi
• 3. Initialize pulse rates ri and the loudness Ai
• 4. whilet < Maximum iterations do
• 5. update velocities and adjust frequencies
• 6. Using Equation (11) Calculate transfer function value
• 7. Using Equation (12) update Xi
• 8. if (rand >ri ) then
• 9. Select the global best solution (Gbest) among the available best solutions and with the available Gbest
dimensions modify the dimensions of Xi randomly
• 10. end
• 11. Generate new solution randomly
• 12. if ((rand <Ai) and (F(Xi)<F(Gbest))then
• 13. Accept the new solutions increase ri and reduce Ai using Equations (9 to 10)
• 14. end
• 15. Find the current Gbest and Rank the best
• 16. end
17. COMPARATIVE ANALYSIS
ALGORITHM BASED ON DEFINED BY FEATURES AREA OF
APPLICATION
BAT Echo location
behavior of bat
Pulse rate emission
and loudness
Accurate and
efficient
Engineering design
and classification
FIREFLY Flashing behavior of
firefly
Brightness and
attractiveness
Finds a good solution
in less number
Digital image
processing
CUCKOO SEARCH Brooding of cuckoo Color of eggs Simple
implementation
Nano technology
18. WHY BAT ALGORITHM IS
BETTER?
1. Automatic zooming.
2. Parameter control.
3. Frequency tuning.
4. More number of solutions.
19. ADVANTAGES OF BAT
• Solve a wide range of problems and highly non-linear problems
efficiently.
• It gives promising optimal solutions.
• The loudness and the pulse emission rates essentially provides a
mechanism for automatic control and auto-zooming into region.
• Number of solutions increases in the library and so more accurate
options are available.
20. IMPROVEMENTS REQUIRED
• Bat algorithm converge quickly at the early stage and then the
convergence rate slows down.
• There is no mathematical analysis to link the parameters with
convergence rates.
• It is not clear what the best values are for most application.