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Hello, friends.

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Today I will tell you how to apply algorithm in life.

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You.

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For that purpose.

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I have a problem that in which our function is ten and 2x1 minus one squared plus 20 x two minus two,

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all squared plus 30 and 2x3 minus three all squared.

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And the objective function used for minimization, I use this so using particle swarm optimization.

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I have to.

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Optimize this.

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Function.

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So in this function we use VSO.

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That is a meta heuristic algorithm.

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All we know that.

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In which population based stochastic search.

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We can also call it population based stochastic search.

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It is based on birds flocking means in which our you can say how the birds fly in a pattern.

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To search out food or to go from one place to another place.

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Search for optimum value.

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And or here.

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And so that is particles from optimization.

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Swarm means population of moving particles and flock means moving together in a crowd.

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In the.

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So we also use search space.

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The third space is the range in which the algorithm computes the optimal control variable.

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So just.

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Go to our function.

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So let's say over here.

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And to solve this problem.

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As you see over here, we have three variables x1x2x3.

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So a number of variables M is three population size, let's say 58wx is 0.9 and W mean is 0.4 and acceleration

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factor.

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I just choose C one and C two is equal to two and the maximum iteration size is 50.

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Okay.

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So how we can implement this function in lab view?

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So for that purpose I just create.

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Or here.

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This is a code.

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For MATLAB.

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As you see, the lower bound is 000 and upper boundaries and 1010 means.

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Using this loop, we can generate a three course five matrix which contains integer values from 0 to

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10.

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As you see over here, I got 8919613, five, ten, ten, two, ten, ten, five, eight.

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So let's just create a variable.

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Add a variable or here, just put this value inside this.

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So 891 first value is.

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Eight nine.

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One second value is.

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961.

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Then over here.

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This is my 961.

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Right now I'm just using the values of this PDF only.

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After that I also tell you how you can compute another values also.

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So right now I'm just using these fixed values.

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Fixed values.

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Three, five, ten.

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Then we have three.

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Five.

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Then after that, I just use 10 to 10.

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10 to 10, and my last row is ten, five, eight.

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NW five and eight.

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So we can generate this function.

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Or you can say this.

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Five cores, three metrics using the random number in this function or here, as you see around is used

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with RAND command around because Rand command is generate values from 0 to 1.

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And over here the limit for this Rand command is from 0 to 10 and using round it will round up my values.

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Like if I got 8.153 it will generate only eight.

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So over here right now in the lab view, as you see over here, I just create visual positions.

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After that I wouldn't want to put.

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I want to go to next step over here.

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Initialize.

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Wait.

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So to initialize it, we have to right now.

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In this function, we require initial population size.

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So initial population is Midas 891 the initial matrix and I want to create initial velocity from it

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and we can generate initial velocity using this formula we 2.1 into.

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This matrix, you can multiply point two, point three on the basis of your requirement.

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Okay.

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Right now I'm just using these 2.1.

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So I just go to my VA.

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In the second step.

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I already fixed this.

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As you see over here, this is my initial position.

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Or you can say initial population.

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Then I multiply it with point one.

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It will generate my metrics, initial velocity or here.

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So when I hit on run button, as you see, I got initial velocity then.

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When I got initial velocity, as you see in 2.1 with my initial five into three metrics.

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And over here this is my variable x1x2x3.

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So I want to generate current position and my current position is x one is called to initial position

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with initial velocity like eight plus point eight, it will give me 8.8.

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So as you see in the lab view, also I got 8.8 over here, my value is nine nine plus point nine.

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I got my current position as you see over here.

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Next step.

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In the next step.

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Over here we have initial population and initial velocity.

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Then.

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We have to.

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Apply our objective function that is used for minimization, that is is to turn into x minus x one all

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squared plus 20 2x2 minus.

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Two.

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All square plus 13.

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Two x.

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Three minus three.

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All square.

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So just go to our library function or here, as you see.

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Here is my x one variable.

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I just separated.

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So let's check how we can separate it.

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So over here, this is my initial velocity.

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And over here, I got my.

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Final value that is.

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Current position.

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This is my current position.

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And so using index as a function, I just separate from zeroth column.

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I got x one from one column I got x two for.

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Second column, I got x three.

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So as you see over here, this is my x1x2x3.

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So right now I want to put this A, b, c.

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And these ABC.

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Comes from.

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Or here.

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So let's put.

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This ABC and this ABC are 1020 and 30.

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These are these are these constants.

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So let's say this is ten, 20 and 30 based on these values, I got the fitness value.

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So the fitness value is 91964.9.

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Let's check for this function as you see.

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1.9649.

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It is n.

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Multiplication of ten factor.

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So over here, if you want to change it 1.9 divided by.

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1000 only.

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So as you see over here, the values are the same.

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Second value is 1.3 to 6.

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Or here I got one 3 to 6 basically in the MATLAB.

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It is this value scores 1.9649 in two.

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Ten plus ten.

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E as you see or hear.

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1.96 49e +03 E +03 means ten res two three.

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So when you multiply this ten days to three with this vector, you got this value that is in.

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Okay, so this is how we get this vector.

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So from here, this step two is completed.

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The objective function or here we have to.

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Find out the minimum value, because in this case we have to minimize.

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So minimum value is 1.3 to 6.

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So second particle we want to work with.

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So over here, as you see for each particle, calculate the velocity, new velocity and new position.

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So new velocity and new position will be calculated using this formula.

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Calculate the velocity v one.

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De plus one is equal to w max that is rate vector v i d plus c one that is average.

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Correction vector into r1x best minus x plus C to r to g best minus x.

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Right.

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So from here you have to just apply this formula.

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So I just go to my code.

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So as you see over here, I got x1x2x3.

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Then over here this part will be create or will create our objective function.

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As you see over here, this x one minus one, all square is multiplied with ax2 minus two, all square

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is multiplied with bx3 minus.

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Or you can also change this to constant.

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It is up to you.

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For this particular function.

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These values are fixed.

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Then you have to find out the minimum of all these three.

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The minimum value will be over here.

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As you see, the minimum value is this from here, the minimum value is second one and the indexing

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for this is one.

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So as you see, index function is V one, because in the live view every iteration starts from zero.

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So Siberia's my .99.9, 6.6, 1.1 and subway array for this means velocity is .9.6.1.

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In the next phase we have to apply this.

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Function.

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We have to calculate the velocity and position of the particle.

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So to calculate the velocity, I just apply this.

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So in this case I just go to phase five or here I want to put the value point nine because maximum value

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is as you see or here.

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This is one nine and C one and Z two is two.

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So just.

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Put these values inside this function.

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See to.

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So when I hit on Run button again, I got.

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x1x2x3 OC.

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So I just increase the size.

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So over here, these values are different from these values because in this case we use random number.

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Random number will generate any value from 0 to 1.

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And in this case, also in the lab view, random number will generate different values.

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So.

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From here.

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Then we have to.

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Apply the pollution for each particle and the solution formula is x i t plus one is to the previous

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position plus velocity.

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That is the same part and or here in the lab view.

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So this is my.

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Updated position, as you see.

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This is my upgraded position.

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And over here, I got new.

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Variables x1x2x3.

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And from here again, we have to find out the fitness value.

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And as you see from here, the minimum value is.

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One five, six, nine.

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But.

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If you want to.

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Check or if you want to compare this fitness value, the fitness tool and fitness or here I got fitness

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one.

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This value is less so both from it will compare from fitness one with fitness two and which value is

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less because the function is minimization.

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It will check best over here.

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And my best is this 136 as you see.

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And my best is 9.9, 6.61.1.

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So using this, my final updated positions are this and initial positions are these as you see.

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So this is how you can or if you want to go through the video.

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So step by step.

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Again, we have to calculate using about our particular formula.

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So it will compare new fitness values.

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And from here, as you see our fitness value, initial fitness value, it will compare with.

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Future fitness value and it will generate best value.

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These are the best value as you see from this value and this value, this one is less.

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It will take it from this value and this value.

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This one is less.

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So it will take this from this value and this value it will.

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It is less so it will take this one.

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So mistakenly it is 7.217.

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It is 2.2179.

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So as you see which one is less, it will take its position.

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So as you see from here, the.

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Initial positions are these and updated positions are these initial positions are 8.8, 9.91.1.

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But the new fitness value for particular one is updated.

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So the positions are 9.8652.

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So using this, you can apply the BSO in live view.

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So this is how you can apply.

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So we just create these functions and lab view.

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So this will apply.

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Your particle swarm optimisation minimization problem.

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And you.

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So this is all about.

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So leave you.

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Thank you.