bacterial foraging optimization
TRANSCRIPT
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Bacterial ForagingOptimization
Group 7
Anches, Harris Joe
Gabrinez, Michael
Tabudlong, Edd Niel
Romero, Ian Lester
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What do you mean by foraging?
Foraging is searching for and exploiting foodresources. It affects an animal's fitness because it
plays an important role in an animal's ability to
survive and reproduce.
Natural selection tends to eliminate those poor
foraging strategies and favor the reproduction of
those animals that have successful foraging
strategies since they are more likely to enjoyreproductive success.
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Bacterial Foraging Optimization
Proposed byPassino
Widely accepted as the global optimization
algorithm of current interest for optimization and
control. Inspired by the social foraging behaviour of
Escherichia Coli, popularly known asE.coli.
Efficient in solving real world optimization
problem arising in several application demands
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Escherichia Coli
4
E.coli
Diameter: 1m
Length: 2m Flagellum:
Counterclockwise:
SwimClockwise:
Tumble
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E.Coli
Counter Clockwise
Rotation
ClockwiseRotation
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Bacterial Foraging Optimization Algorithm
Steps for BFOA
a) Chemotaxis
b) Swarming
c) Reproduction
d) Elimination / dispersal
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a.) Chemotaxis This process simulates the movement of anE.coli cell
through swimming and tumblingvia flagella.
Depending upon the rotation of the flagella in each
bacterium, it decides whether it should move in a
predefined direction (swimming) or an altogether different
direction (tumbling), in the entire lifetime of the bacterium.
Bacterial Foraging Optimization Algorithm
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a.) Chemotaxis Suppose (j, k, l) i q represents i-th bacterium at jth
chemotactic, k-th reproductive and l-th elimination-
dispersal step. C(i) is the size of the step taken in the
random direction specified by the tumble (run length unit).
Bacterial Foraging Optimization Algorithm
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b.) Swarming It is always desired that the bacterium that has searched the
optimum path of food should try to attract other bacteria so
that they reach the desired place more rapidly. Swarming
makes the bacteria congregate into groups and hence moveas concentric patterns of groups with high bacterial
density. Mathematically, swarming can be represented by
Bacterial Foraging Optimization Algorithm
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whereJcc (, P(j, k, l)) is the cost function value to be added
to the actual cost function to be minimized to present a time
varying cost function. S is the total number of bacteria. p
is the number ofparameters to be optimized that are present ineach bacterium. dattract, attract, hrepelent, andrepelentare
different coefficients that are to be chosen judiciously.
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Visual demonstration of BFO
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c.) Reproduction
The least healthy bacteria die, and the other
healthiest bacteria each split into two bacteria,
which are placed in the same location. This makesthe population of bacteria constant.
Bacterial Foraging Optimization Algorithm
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d.) Elimination
Dispersal It is possible that in the local environment, the life of a
population of bacteria changes either gradually by
consumption of nutrients or suddenly due to some other
influence. Events can kill or disperse all the bacteria in aregion. They have the effect of possibly destroying the
chemotactic progress, but in contrast, they also assist it,
since dispersal may place bacteria near good food sources.
Elimination and dispersal helps in reducing the behaviourofstagnation (i.e., being trapped in a premature solution
point or local optima).
Bacterial Foraging Optimization Algorithm
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START
InitializeParameters
Increaseelimination-
dispersion
loop counter
l = l+1
l < NedSTOPNo
Increase
reproduction
loop counter
k = k+1
Yes
k
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k< Nre
Increase
chemotactic
loop counter
j = j+1
Yes
k
j< NcYes
W
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compute
J(i,j,k,l) set
Jlast = J(i,j,k,l)
Increasebacterium
index
i = i + 1
Yes
W
tumble
C
i < Scompute
J(i,j+1,k,l)
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m = m + 1
No
Set swimcounter m=0
Yes
B
C
Set Jlast =
J(I,j+1,k,l)
m < Ns
J(i,j,k,l)
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compute
J(i,j,k,l) set
Jlast = J(i,j,k,l)
No
Increasebacterium
index
i = i + 1
Yes
X
W
B
tumble
C
i < Scompute
J(i,j+1,k,l)
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l < Nre
Increase
chemotactic
loop counter
j = j+1
Yes
k
X
l < NcNo Yes
W
Perform
Reproduction
Y
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START
InitializeParameters
Increaseelimination-
dispersion
loop counter
l = l+1
l < NedSTOPNo
Increase
reproduction
loop counter
k = k+1
Yes
Z
Y
k
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l < NrePerform
elimination
dispersal
No
Increase
chemotactic
loop counter
j = j+1
Yes
Z
k
X
l < NcNo Yes
W
Perform
Reproduction
Y
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START
InitializeParameters
Increaseelimination-
dispersion
loop counter
l = l+1
l < NedSTOPNo
Increase
reproduction
loop counter
k = k+1
Yes
k
Z
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Pseudocode [Step 1] Initialize parametersp, S, Nc, Ns, Nre, Ned, Ped,C(i)(i=1,2S),i.
p: Dimension of the search space,
S: Total number of bacteria in the population, Nc : The number of chemotactic steps,
Ns: The swimming length.
Nre : The number of reproduction steps,
Ned : The number of elimination-dispersal events,
Ped : Elimination-dispersal probability,
C (i): The size of the step taken in the random direction
specified by the tumble.
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[b] Compute fitness function, J (i, j, k, l).
Let,J (i, j, k, l)= J (i, j, k, l)+ J cc(i( j, k, l),P( j, k, l)) (i.e.
add on the cell-to cell attractantrepellant profile to
simulate the swarming behavior)
whereJcc is the cost function value to be added to the
actual cost function to be minimized to present a time
varying cost function.
Bacterial Foraging Optimization Algorithm
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Bacterial Foraging Optimization Algorithm
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Bacterial Foraging Optimization Algorithm
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Bacterial Foraging Optimization Algorithm
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Bacterial Foraging Optimization Algorithm
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APPLICATION
Optimization over continuous surfaces Algorithmic extension: Hybrid appoach
Comparative analysis with other methods Particle Swarm
Optimization in particular.
Adaptive control: Introduction of the idea and applicationto liquid level control.
Proportional-Integral-Derivative (PID) controller tuning
Harmonic estimation
Active power filter for load optimization
Transmission loss reduction: Application to Power System
Optimizing power loss and voltage stability limits
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Thank You!