tlbo.rar_MATLAB TLBO_TLBO_tlbo algorithm_tlbo program_tlbo算法程序

function [globalparameters,globalmins]=tlbo(T,seq,indsol,ng,ni,runtimes) [jobnum,macnum]=size(T); oprnum=jobnum*macnum; globalmins=zeros(runtimes,1); globalparameters=zeros(runtimes,oprnum); for R=1:runtimes %%%% number of induvidual solutions or population in algorithm [jobnum,macnum]=size(T); oprnum=jobnum*macnum; %%%% total number of operations solutions=zeros(indsol,oprnum); %%%% processing sequence for individual solutions number=zeros(1,jobnum); for i=1:jobnum number(i)=macnum; end for j=1:indsol %%% initial solutions jobopr=number; for i=1:oprnum val=unidrnd(jobnum); while jobopr(val)==0 val=unidrnd(jobnum); end solutions(j,i)= val; %%%% processing sequence for individual solutions jobopr(val)=jobopr(val)-1; end end %%%%% calculating %%%%%% %%%%%% best solution, minimum makespan value and process sequence for that value %%%%%% makespan for individual solutions makespan=zeros(indsol,1); for i=1:indsol %%% initial makespan : [makespan1]=makespancal(s,seq,T) %%%%%%%%%%%%%%%%%%%% s=solutions(i,:); [makespan1]=makespancal(s,seq,T); makespan(i,1)=makespan1; end bestindex=find(makespan==min(makespan)); globalmin=makespan(bestindex); globalparameter=solutions(bestindex,:); true=1; while(true) ni=ni+1; %%%%% teachers phase bigins %%%%%%% sorted_span=sort(makespan); index=(fix(indsol/2)); mean_makespan=sorted_span(index,:); mindex=find(makespan==mean_makespan); md=solutions(mindex(1,1),:); md_makespan=makespan(mindex(1,1),:); indext=find(makespan==min(makespan)); teacher=solutions(indext(1,1),:); teacher_makespan=makespan(indext(1,1),:); a=teacher(1,1:oprnum); b=md(1,1:oprnum); [newsolution,newmakespan] = pbc(T,a,b,seq); diff=newsolution; diff_makespan=newmakespan; Chrom=solutions; teachersolutions=zeros(indsol,oprnum); tmakespan=zeros(indsol,1); for i=1:indsol %%%% finding teacher's phase solutions a=Chrom(i,1:oprnum); b=diff(1,1:oprnum); [newsolution,newmakespan] = pbc(T,a,b,seq); teachersolutions(i,:)=newsolution; tmakespan(i,1)=newmakespan; end for i=1:indsol %%% applying greedy selection process at teacher's phase if(makespan(i,1)>tmakespan(i,1)) solutions(i,:)=teachersolutions(i,:); % updating the solutions makespan(i)=tmakespan(i); end end %%%%%%%%%%%%% learners phase %%%%%%%%%%%%%%%%% indexl=find(makespan==min(makespan)); bl=indexl; best_learner=solutions(bl(1,1),:); best_learnermakespan=makespan(bl(1,1),1); [lsolutions,lmakespan] = TLBOvariablesearch(T,seq,solutions,makespan,indsol); learner_solutions=lsolutions; learner_makespan=lmakespan; for i=1:indsol %%% applying greedy selection process at learner's phase if(makespan(i,1)>learner_makespan(i,1)) solutions(i,:)=learner_solutions(i,:); % updating the solutions makespan(i)=learner_makespan(i); end end ind=find(makespan==min(makespan)); % best food source position is captured ind=ind(end); if (makespan(ind)<globalmin) globalmin=makespan(ind); globalparameter=solutions(ind,:); end if(ni==ng) ni=0; true=0; else true=1; end fprintf('no of runs =%d num of iterations =%d Global min=%g\n',R,ni,globalmin); end globalparameters(R,:)=globalparameter(:,:); globalmins(R,1)=globalmin; disp('globalmins') disp(globalmins) end