How do I solve this problem at the optimization toolbox?

Question

민제 강 2021-7-1

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/869308-how-do-i-solve-this-problem-at-the-optimization-toolbox

回答： Star Strider 2021-7-1

function Y = objective_function(X) 
x1=X(1,1);
x2=X(1,2);
       p00 =  -9.601e+04;
       p10 =       -1031;
       p01 =       -4854;
       p20 =        8810;
       p11 =       404.4;
       p02 =       359.8;
       p21 =      -228.4;
       p12 =       165.8;
       p03 =       356.1;
       
       Y = p00 + p10*x(1) + p01*x(2) + p20*x(1).^2 + p11*x(1)*x(2) + p02*x(2).^2 + p21*x(1).^2*x(2) + p12*x(1)*x(2).^2 + p03*x(2).^3;
end

This is my code.

When I run a optimization toolbox, a problem such as the picture occur.

I'd appreciate your help. Thank you!

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Star Strider 2021-7-1

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/869308-how-do-i-solve-this-problem-at-the-optimization-toolbox#answer_737098

在 MATLAB Online 中打开

It would llikely be easier to just call ga from a script —

lb = [-10 -6];
ub = [ 10  6];
nvars = 2;
[Xr,fval,exitflag,output,population,scores] = ga(@objective_function, nvars, [], [], [], [], lb, ub)
Optimization terminated: average change in the fitness value less than options.FunctionTolerance.
Xr = 1×2
   -0.1234   -6.0000
fval = -1.3101e+05
exitflag = 1
output = struct with fields:
      problemtype: 'boundconstraints'
         rngstate: [1×1 struct]
      generations: 97
        funccount: 4612
          message: 'Optimization terminated: average change in the fitness value less than options.FunctionTolerance.'
    maxconstraint: 0
       hybridflag: []
population = 50×2
   -0.1234   -6.0000
   -0.1234   -6.0000
   -0.1234   -6.0000
   -0.1234   -6.0000
   -0.1234   -5.9999
   -0.1234   -6.0000
   -0.1234   -6.0000
   -0.1234   -6.0000
   -0.1234   -6.0000
   -0.1234   -6.0000
scores = 50×1
	1.0e+05 *

   -1.3101
   -1.3101
   -1.3101
   -1.3101
   -1.3100
   -1.3101
   -1.3101
   -1.3101
   -1.3101
   -1.3101
function Y = objective_function(X) 
x(1)=X(1,1);
x(2)=X(1,2);
       p00 =  -9.601e+04;
       p10 =       -1031;
       p01 =       -4854;
       p20 =        8810;
       p11 =       404.4;
       p02 =       359.8;
       p21 =      -228.4;
       p12 =       165.8;
       p03 =       356.1;
       
       Y = p00 + p10*x(1) + p01*x(2) + p20*x(1).^2 + p11*x(1)*x(2) + p02*x(2).^2 + p21*x(1).^2*x(2) + p12*x(1)*x(2).^2 + p03*x(2).^3;
end