GA Optimization - supplying different constraints depending on loop cycle

Hello everyone,

I am trying to minimize my fitness function with the genetic algorithm. I have already set linear inequality constraints via the matrices A und b which are working fine (x0(1) < x0(2), x0(2) < x0(3), ....).

Now I want to add constraints so that the values computed for the fitness function don't get too high - speed should be <= 20 and range <= 10. Basically my code looks like as in the following:

function [out] = Optimization(x0)

variabel_Vektor = [x0(1) x0(2) x0(3) x0(4) x0(5) x0(6) x0(7) x0(8) x0(9)];

loop_1 = [4 3 2 1];
loop_2 = 1.0:-0.1:0.2; % length(1.0:-0.1:0.2) = 9, so x0 goes up to x0(9)


for S_1 = 1:length(loop_1)
    
    for S_2 = 1:length(loop_2)
        
        % various calculations follow here
        % [.........]
        
        
        if distance >= 0
           range(S_1,S_2) = s_T - [(s_T - x0(S_2) - l - d) + s_B];
        
        else range(S_1,S_2) = 0;
        end
        
        
        if distance < 0
           speed(S_1,S_2) = sqrt(v0^2 - 2*loop_1(S_1)*g*[s_T-(s_T-x0(S_2)-l-d)]);

        else speed(S_1,S_2) = 0;
        end
        
        
    end
end


% fitness function
out = sum(speed(:))+sum(range(:));

The constraints I want to add are the following:

% constraints for speed: each <= 20: 
  sqrt(v0^2 - 2*loop_1(S_1)*g*[s_T-(s_T-x0(S_2)-l-d)]) - 20 <= 0

% constraints for range: each <= 10:
  s_T - [(s_T - x0(S_2) - l - d) + s_B] - 10 <= 0

For speed, I get a nonlinear inequality constraint and for range a linear inequality constraint. I don't know how to pass these constraints to matlab because in the expressions there is a dependency on the respective loop pass (S_2 for x0 and S_1).

Does anyone have an idea? I would really appreciate it! Thanks a lot.