Hi Sajad
To implement a constraint on the variable beta4 such that it lies within a specific range ( A < beta4 < B ) in a genetic algorithm using MATLAB, you can use nonlinear constraints. The ga function allows you to specify nonlinear constraints using a function handle.
Here's how you can modify your setup to include this constraint:
1.Define a Nonlinear Constraint Function:
Create a function that returns two outputs: c and ceq. The c output is for inequality constraints (i.e., constraints of the form ( c(x) <= 0 )), and ceq is for equality constraints (i.e., constraints of the form ( ceq(x) = 0 )). For your requirement, you will only need to use c.
function [c, ceq] = nonlcon(x)
% Extract your variables
kisi = x(1);
phi = x(2);
psi = x(3);
% Calculate U4, Cm4, and beta4 as in your fitfun
U4 = sqrt(deltaH0/psi);
Cm4 = phi * kisi * U4;
beta4 = atand((Cu4 - U4) / Cm4);
% Define the inequality constraints for beta4
A = ...; % Specify the lower bound
B = ...; % Specify the upper bound
c = [A - beta4; beta4 - B]; % Ensure A < beta4 < B
% No equality constraints
ceq = [];
end
2. Update the ga Function Call:
Ensure that you pass this nonlinear constraint function to the ga function.
options = optimoptions('ga', 'FunctionTolerance', 1e-2, 'PlotFcn', @gaplotbestf, 'MaxGenerations', 50);
[x, fval, exitflag, output] = ga(@(x)fitfun(x), nvars, A, b, Aeq, beq, lb, ub, @nonlcon, IntCon, options);
By following these steps, you can enforce the constraint on beta4 within your genetic algorithm optimization process.
I hope this helps!
Sameer
