genetic_op.m

基于微分的多目标优化问题

function ind=genetic_op(subproblems, index, domain, params)
%GENETICOP function implemented the DE operation to generate a new
%individual from a subproblems and its neighbours.

%   subproblems: is all the subproblems.
%   index: the index of the subproblem need to handle.
%   domain: the domain of the origional multiobjective problem.
%   ind: is an individual structure.
    neighbourindex = subproblems(index).neighbour;
    
    %The random draw from the neighbours.
    nsize = length(neighbourindex);
    si = ones(1,3)*index;
    
    si(1)=neighbourindex(ceil(rand*nsize));
    while si(1)==index
        si(1)=neighbourindex(ceil(rand*nsize));
    end
    
    si(2)=neighbourindex(ceil(rand*nsize));
    while si(2)==index || si(2)==si(1)
        si(2)=neighbourindex(ceil(rand*nsize));
    end
    
    si(3)=neighbourindex(ceil(rand*nsize));
    while si(3)==index || si(3)==si(2) || si(3)==si(1)
        si(3)=neighbourindex(ceil(rand*nsize));
    end
     
    %retrieve the individuals.
    points = [subproblems(si).curpoint];
    selectpoints = [points.parameter];
    
    oldpoint = subproblems(index).curpoint.parameter;
    parDim = size(domain, 1);
    
    jrandom = ceil(rand*parDim);
    
    randomarray = rand(parDim, 1);
    deselect = randomarray<params.CR;
    deselect(jrandom)=true;
    newpoint = selectpoints(:,1)+params.F*(selectpoints(:,2)-selectpoints(:,3));
    newpoint(~deselect)=oldpoint(~deselect);
    
    %repair the new value.
    newpoint=max(newpoint, domain(:,1));
    newpoint=min(newpoint, domain(:,2));
    
    ind = struct('parameter',newpoint,'objective',[], 'estimation',[]);
    %ind.parameter = newpoint;
    %ind = realmutate(ind, domain, 1/parDim);
    ind = gaussian_mutate(ind, 1/parDim, domain);
    
    %clear points selectpoints oldpoint randomarray deselect newpoint neighbourindex si;
end