Please help me to using genetic algorithm
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
I write this code but I want to solve this problem with 'ga' not with 'intlinprog' solver!
Can anyone guide me?
costprob = optimproblem;
% Indices
k = 15;
j = 2;
f = 10;
l = 5;
r0 = 6;
r = 6;
% Parameters
cr0 = 0 + 1*rand(1,r0);
dr0f = 0 + 1*rand(r0,f);
csl = 0 + 1*rand(1,l);
DE = 200 + 100*rand(1,1);
csur0f = 2000 + 1000*rand(r0,f);
ctl = 1000 + 1000*rand(1,l);
cvl = 10 + 10*rand(1,l);
cpjk = 0 + 1*rand(j,k);
corj = 0 + 1*rand(r,j);
pr0f = 0 + 1*rand(r0,f);
vjk = 0 + 1*rand(j,k);
cvjrk = 0 + 1*rand(j,r,k);
M = 10000000000000;
% Variables
xl = optimvar('xl',1,l,'LowerBound',0);
yr0f = optimvar('yr0f',r0,f,'Type','integer','LowerBound',0,'UpperBound',1);
xx1r0f = optimvar('xx1r0f',r0,f,'LowerBound',0);
xx2r0f = optimvar('xx2r0f',r0,f,'LowerBound',0);
yjk1 = optimvar('yjk1',j,k,'Type','integer','LowerBound',0,'UpperBound',1);
yl2 = optimvar('yl2',1,l,'Type','integer','LowerBound',0,'UpperBound',1);
zjkr = optimvar('zjkr',j,k,r,'LowerBound',0);
wrj = optimvar('wrj',r,j,'LowerBound',0);
% Objective function
objfun1 = sum(sum(dr0f.*xx1r0f,2).*cr0',1);
objfun2 = sum(sum(corj.*wrj,2),1);
objfun3 = sum(sum(pr0f.*xx1r0f,2),1);
objfun4 = sum(sum(cpjk.*yjk1,2),1);
objfun5 = sum(csl.*xl,2);
costprob.Objective = objfun1 + objfun2 + objfun3 + objfun4 + objfun5;
% Constraints
cons1 = sum(xl,2) >= DE;
cons2 = sum(xl,2)*ones(j,1,r) == sum(zjkr,2);
cons3 = xx1r0f <= csur0f.*yr0f;
cons4 = xl <= ctl.*yl2;
cons5 = xl >= cvl.*yl2;
cons6 = sum(yjk1,2) == ones(j,1);
cons7 = squeeze(sum(zjkr,3)) <= M*yjk1;
cons8 = (1-dr0f).*xx1r0f == xx2r0f;
costprob.Constraints.cons1 = cons1;
costprob.Constraints.cons2 = cons2;
costprob.Constraints.cons3 = cons3;
costprob.Constraints.cons4 = cons4;
costprob.Constraints.cons5 = cons5;
costprob.Constraints.cons6 = cons6;
costprob.Constraints.cons7 = cons7;
costprob.Constraints.cons8 = cons8;
2 Kommentare
Akzeptierte Antwort
Matt J
am 21 Okt. 2019
Bearbeitet: Matt J
am 21 Okt. 2019
You can use prob2struct to obtain most of your problem parameters in solver form,
problem=prob2struct(costprob);
problem=rmfield(problem,'solver');
problem.nvars=numel(problem.lb);
problem.fitnessfcn=@(x) dot(problem.f,x);
x=ga(problem);
However your problem, as currently formulated, has both integer and equality constraints, which ga cannot handle. See here, for guidelines on how to rewrite the problem without equality constraints:
5 Kommentare
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Genetic Algorithm finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!