bacterial foraging optimization

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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.

    Bacterial Foraging Optimization Algorithm

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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!