Genetic algorithm problem in matrix index

조회 수: 2 (최근 30일)
tahseen alshmary
tahseen alshmary 2018년 12월 30일
댓글: tahseen alshmary 2018년 12월 31일
Dear sir
i have problem with this code . can any one help me and thanks advance
Index exceeds matrix dimensions.
Error in Untitled3 (line 47)
CTI1 = A(1,1)*x(1)+A(1,6)*x(6)
the code
%To minimize our fitness function using the ga function, we need to pass in a function handle to the fitness function as well as specifying the number of variables as the second argument. Lower and upper bounds are provided as LB and UB respectively. In addition, we also need to pass in a function handle to the nonlinear constraint function.
ObjectiveFunction = @simple_fitness;
nvars = 14; % Number of variables
LB = [0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05]; % Lower bound for (x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14)
UB = [1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ]; % Upper bound for (x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14)
A=zeros(20,14); b=zeros(20,1);
A(1,1)=+2.174; A(1,6)=-1.777; b(1)=-0.3; %first row
A(2,1)=-3.555; A(2,2)=+1.159; b(2)=-0.3; %second row
A(3,2)=+1.159; A(3,7)=-2.137; b(3)=-0.3; %third row
A(4,2)=-1.272; A(4,3)=+2.880; b(4)=-0.3; %fourth row
A(5,3)=-3.593; A(5,4)=+3.043; b(5)=-0.3; %fifth row
A(6,4)=-3.818; A(6,5)=+1.402; b(6)=-0.3; %sixth row
A(7,5)=-1.650; A(7,6)=+1.519; b(7)=-0.3; %seventh row
A(8,5)=-1.650; A(8,7)=+1.606; b(8)=-0.3; %eighth row
A(9,6)=+1.519; A(9,14)=-2.143; b(9)=-0.3; %nighth row
A(10,7)=+1.606; A(10,13)=-2.629; b(10)=-0.3; %tenth row
A(11,7)=-2.137; A(11,8)=+1.806; b(11)=-0.3; %eleventh row
A(12,8)=+1.806; A(12,9)=-1.622; b(12)=-0.3; %tweleventh row
A(13,9)=+1.948; A(13,10)=-3.605; b(13)=-0.3; %thirteenth row
A(14,10)=+2.753; A(14,11)=-3.935; b(14)=-0.3; %fourteenth row
A(15,11)=+3.105; A(15,12)=-1.287; b(15)=-0.3; %fiveteenth row
A(16,12)=+1.181; A(16,13)=-2.629; b(16)=-0.3; %sixteenth row
A(17,12)=+1.181; A(17,14)=-2.143; b(17)=-0.3; %seventennth row
A(18,8)=-2.232; A(18,13)=+1.816; b(18)=-0.3; %eighteenth row
A(19,1)=-3.555; A(19,14)=+1.608; b(19)=-0.3; %nighnteenth row
A(20,9)=-1.622; A(20,14)=+1.608; b(20)=-0.3; %twinty row
rng default % For reproducibility
options=gaoptimset('plotfcns',{'gaplotgenealogy','gaplotselection', 'gaplotbestf' ,@gaplotscorediversity});
options= gaoptimset(options,'Tolfun',1e-6,'TolCon',1e-3,'stallGenlimit',50);
options= gaoptimset(options,'MutationFcn',{@mutationadaptfeasible});
options=gaoptimset(options,'Display', 'iter');
[x,fval,exitflag,output,population,scores] = ga(ObjectiveFunction,nvars,A,b,Aeq,beq,LB,UB,nonlcon,IntCon,options)
CTI1 = A(1,1)*x(1)+A(1,6)*x(6)
CTI2 = A(2,1)*x(1)+ A(2,2)*x(2)
CTI3 = A(3,2)*x(2)+ A(3,7)*x(7)
CTI4 = A(4,2)*x(2)+ A(4,3)*x(3)
CTI5 = A(5,3)*x(3)+ A(5,4)*x(4)
CTI6 = A(6,4)*x(4)+ A(6,5)*x(5)
CTI7 = A(7,5)*x(5)+ A(7,6)*x(6)
CTI8 = A(8,5)*x(5)+ A(8,7)*x(7)
CTI9 = A(9,6)*x(6)+ A(9,14)*x(14)
CT10 = A(10,7)*x(7)+ A(10,13)*x(13)
CTI11= A(11,7)*x(7)+ A(11,8)*x(8)
CTI12= A(12,8)*x(8)+ A(12,9)*x(9)
CTI13= A(13,9)*x(9)+ A(13,10)*x(10)
CTI14= A(14,10)*x(10)+ A(14,11)*x(11)
CTI15= A(15,11)*x(11)+ A(15,12)*x(12)
CTI16= A(16,12)*x(12)+ A(16,13)*x(13)
CTI17= A(17,12)*x(12)+ A(17,14)*x(14)
CTI18= A(18,8)*x(8)+ A(18,13)*x(13)
CTI19= A(19,1)*x(1)+ A(19,14)*x(14)
CTI20= A(20,9)*x(9)+ A(20,14)*x(14)

채택된 답변

Stephan 2018년 12월 30일
편집: Stephan 2018년 12월 30일
you are using the simple_fitness function, which is an implementation of the Rosenbrock function. This function is a built in example function and has a number of variables = 2. How do you expect your code to run on 14 variables with this function?
Maybe you did write your own fitness function, which is accidently named like the built in one. If so, rename the .m-file you created and provide the function together with the other code if there are still more issues.
Best regards
  댓글 수: 9
Stephan 2018년 12월 30일
편집: Stephan 2018년 12월 30일
i wish you success, please accept useful answers.
tahseen alshmary
tahseen alshmary 2018년 12월 31일
thanks you so much

댓글을 달려면 로그인하십시오.

추가 답변 (0개)


Help CenterFile Exchange에서 Genetic Algorithm에 대해 자세히 알아보기


Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by