Different Output value of Functions

function f_value = test_function(Solution)

% Beale function test functions

Implementation 1

x=Solution(1);

y=Solution(2);

f_value = (1.5- x+ x*y)^2 + (2.25 - x + x*y^2)^2 + (2.625 -x + x*y^2)^2;

% Optimal at f(3,0.5) = 0

%% Implementation 2

% x1 = Solution(1);

% x2 = Solution(2);

%

% term1 = (1.5 - x1 + x1*x2)^2;

% term2 = (2.25 - x1 + x1*x2^2)^2;

% term3 = (2.625 - x1 + x1*x2^3)^2;

%

% f_value = term1 + term2 + term3;

end

Hi, I have a test function for testing my optimization algorithm. These two implementations are same but they are generating different f_value for the same solution value. For example. for Solution [-3.3381,0.1668], Implementation1 generates f_value of 82.9832 whereas Imp2 generates 83.8981. I am unable to find the reason. This small difference is making huge difference and with imp1. Optimization algorithm doesnot converge to global solution. However, with imp2 it converges to global solution. Any help would be appreciated.

Answer 1

[Voss]

Implementation 1 has x*y^2 in the third term; Implementation 2 has x1*x2^3 in term3.

f_value = (1.5- x+ x*y)^2 + (2.25 - x + x*y^2)^2 + (2.625 -x + x*y^2)^2;
%                                                                  ^ squared


term1 = (1.5 - x1 + x1*x2)^2;
term2 = (2.25 - x1 + x1*x2^2)^2;
term3 = (2.625 - x1 + x1*x2^3)^2;
%                           ^ cubed

f_value  = term1 + term2 + term3;

[Khalid Khawaja]

Thank you very much. Problem solved. I don't know why I couldn't see this. Now the algorithm has converged to global optimal 3.00016245036652 0.500051623977431. Implementation 1 had a typo. Squared term instead of a cubed.

[Voss]

You're welcome!