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For the optimization of constraint conditions with summation function, why do you change the objective function and the result is still the same

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Ask for guidance and check this code , Change objective function ,XYZ Value

```

[X,Y,Z] = meshgrid(6:12,6:.1:20,8:30);
F = piX.Y.2.(500+Z)7850127505.5+piZ.2.50078501275026.5;% Objective function
C = true(size(X));
for i = 1:numel(X)
n = X(i);% x(1)
y = Y(i);% x(2)
z = Z(i);
c = 0;
for k = 1:1:n
c = c+((16
y (500+z+(k-1)y)^2 ))/((500+(k-1)y)((500+z+(k-1)y)^2-250000));
end
x = n;
C(i) = ((31.4
(3^0.5))
((500+x
y+z)/500)^2)/((((500+xy+z)/500)^2)-1)-(z134.2)/(xy+z)-((177.27xy)/(xy+z))-2c<=0;% constraint condition
end
minf = min(F(C));
if(isempty(minf))
fprintf(' unsolvable \n')
else
I = find((minf==F)&C);
x = X(I);
y = Y(I);
z = Z(I);
fmin =pi
x.y.2.(500+z)7850127505.5+piz.500278501275026.5;% Results output
fprintf(' stay x=%d,y=%d,z=%d The objective function at has a minimum value %d\n',x,y,z,fmin)
end
F(~C)=NaN;
scatter3(X(:),Y(:),Z(:),10,F(:))

``` The result is the same every time

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