Input: dilatation G
Output: minimal node set contented to target optimization
MAXGEN = 100; //maximal genetic algebra;
GGAP = 0.9; //generation gap;
trace = zeros(MAXGEN, 2); //initiate value of genetic algorithm ability tracking;
Repeatedly run algorithm in Algorithm 4 to generate a enough big feasible path set ;
Chrom = ; //initiate group
gen = 0;
ObjV = Target(Chrom); //calculate target function value (number of nodes) of initiate group;
FitnV = ranking(ObjV); //allocate accommodation value
SelCh = select(“sus”, TestChrom, FitnV, GGAP); //chose
SelCh = recombin(“xovsp”, SelCh, 0.7); //recombinant
SelCh = mutbga(SelCh, f); SelCh = fix(SelCh); //variation
ObjVSel = Target(SelCh); //calculate target function value in descendant
Chrom ObjV = reins(Chrom, SelCh, 1, 1, ObjV, ObjVSel); //re-insert
gen = gen + 1;
remove individual in descendant which can not totally cover target set;
descendant after removal of un-contented individual named as Chrom2;
ObjV2 = Target(Chrom2);
trace(gen, 1) = min(ObjV2);
trace(gen, 2) = mean(ObjV2);
RETURN minimal node set which can cover T through self-direction
Algorithm 5: Multinode multi-interested target self-direction optimal coverage discriminate algorithm based on genetic algorithm.