如下,要判断时候为正数,这个搞了好久
我本来是用lingo做的,lingo也是老报错
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我奋斗了好久,只能说是书到用时方恨少.搜索了下,我用了lingo以及5月1号写的那些长长的程序后,没有什么效果,我用lingo老是出错,太久没用了,我也查不出哪里错了.lingo的程序在下楼.MATLAB之前写的程序也在下楼.
这次我是用遗传算法解决,可是每次运行的结果都不一样,而且结果也不是我要的.我乱套用这些,可是没办法.这个问题老是解决不了(问题function.jpg如图).下面是我的程序,不知道我的什么时候能做完,嗨,我的5.1节啊.天亮后去下图书馆,找下lingo该怎么解决.觉得MATLAB来解决这个问题太难啦~~~
%------------------------gainteger_GA.m--------------------------------------
function [x,fval] = gainteger_GA
%This is a function to solve the problem of interger programming using GA
%function in the Genetic Algorithm and Direct Search Toolbox
% Fitness function and numver of variables
fitnessFcn = @(x) gaf(x);
numberOfVariables = 12;
% If decision variables are bounded provide a bound e.g, LB and UB.
LB = -100*ones(1,numberOfVariables);
UB = 100*ones(1,numberOfVariables);
Bound = [LB;UB]; % If unbounded then Bound = []
% Create an options structure to be passed to GA
% Three options namely 'CreationFcn', 'MutationFcn', and
% 'PopInitRange' are required part of the problem.
options = gaoptimset('CreationFcn',@int_pop,'MutationFcn',@int_mutation, ...
'PopInitRange',Bound,'Display','iter','StallGenL',40,'Generations',150, ...
'PopulationSize',60,'PlotFcns',{@gaplotbestf,@gaplotbestindiv});
[x,fval] = ga(fitnessFcn,numberOfVariables,options);
%---------------------------------------------------
% Mutation function to generate childrens satisfying the range and integer
% constraints on decision variables.
function mutationChildren = int_mutation(parents,options,GenomeLength, ...
FitnessFcn,state,thisScore,thisPopulation)
shrink = .01;
scale = 1;
scale = scale - shrink * scale * state.Generation/options.Generations;
range = options.PopInitRange;
lower = range(1,:);
upper = range(2,:);
scale = scale * (upper - lower);
mutationPop = length(parents);
% The use of ROUND function will make sure that childrens are integers.
mutationChildren = repmat(lower,mutationPop,1) + ...
round(repmat(scale,mutationPop,1) .* rand(mutationPop,GenomeLength));
% End of mutation function
%---------------------------------------------------
function Population = int_pop(GenomeLength,FitnessFcn,options)
totalpopulation = sum(options.PopulationSize);
range = options.PopInitRange;
lower= range(1,:);
span = range(2,:) - lower;
% The use of ROUND function will make sure that individuals are integers.
Population = repmat(lower,totalpopulation,1) + ...
round(repmat(span,totalpopulation,1) .* rand(totalpopulation,GenomeLength));
% End of creation function
%----------------------------------------------------------------------------------
另外一个函数:
%----------------------------gaf.m--------------------------------------------
function f=gaf(x)
v=1/2*[1.1 1.1 1.2 1.2 1.3 1.3];
xs=[42 32 41 67 25 29];
if((x(7)==49)&&(x(6)+x(12)-xs(6)==0)&&(x(8)-x(7)+xs(1)-x(1)==0) ...
&&(x(9)-x(8)+xs(2)-x(2)==0)&&(x(10)-x(9)+xs(3)-x(3)==0) ...
&&(x(11)-x(10)+xs(4)-x(4)==0)&&(x(12)-x(11)+xs(5)-x(5)==0) ...
&&(x(1)>=0)&&(x(2)>=0)&&(x(3)>=0)&&(x(4)>=0)&&(x(5)>=0)&&(x(6)>=0))
f=-(48*x(1)+48*x(2)+48*x(3)+48*x(4)+48*x(5)+48*x(6)-v(1)*x(1)^2 ...
-v(2)*x(2)^2-v(3)*x(3)^2-v(4)*x(4)^2-v(5)*x(5)^2-v(6)*x(6)^2 ...
-0.1*(x(7)>0)*x(7)-0.1*(x(8)>0)*x(8)-0.1*(x(9)>0)*x(9)-0.1*(x(10)>0)*x(10) ...
-0.1*(x(11)>0)*x(11)-0.1*(x(12)>0)*x(12));
else,f=10000;
end
[本帖最后由 fouvy 于 -5-2 03:13 编辑]
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