算法源码-优化与控制：普通多目标优化算法代码.zip

function pareto = moead( mop, varargin) %MOEAD runs moea/d algorithms for the given mop. % Detailed explanation goes here % The mop must to be minimizing. % The parameters of the algorithms can be set through varargin. including % popsize: The subproblem's size. % niche: the neighboursize, must less then the popsize. % iteration: the total iteration of the moead algorithms before finish. % method: the decomposition method, the value can be 'ws' or 'ts'. starttime = clock; %global variable definition. global params idealpoint objDim parDim itrCounter; %set the random generator. rand('state',10); %Set the algorithms parameters. paramIn = varargin; [objDim, parDim, idealpoint, params, subproblems]=init(mop, paramIn); itrCounter=1; while ~terminate(itrCounter) tic; subproblems = evolve(subproblems, mop, params); disp(sprintf('iteration %u finished, time used: %u', itrCounter, toc)); itrCounter=itrCounter+1; end %display the result. pareto=[subproblems.curpoint]; pp=[pareto.objective]; scatter(pp(1,:), pp(2,:)); disp(sprintf('total time used %u', etime(clock, starttime))); end function [objDim, parDim, idealp, params, subproblems]=init(mop, propertyArgIn) %Set up the initial setting for the MOEA/D. objDim=mop.od; parDim=mop.pd; idealp=ones(objDim,1)*inf; %the default values for the parameters. params.popsize=100;params.niche=30;params.iteration=100; params.dmethod='ts'; params.F = 0.5; params.CR = 0.5; %handle the parameters, mainly about the popsize while length(propertyArgIn)>=2 prop = propertyArgIn{1}; val=propertyArgIn{2}; propertyArgIn=propertyArgIn(3:end); switch prop case 'popsize' params.popsize=val; case 'niche' params.niche=val; case 'iteration' params.iteration=val; case 'method' params.dmethod=val; otherwise warning('moea doesnot support the given parameters name'); end end subproblems = init_weights(params.popsize, params.niche, objDim); params.popsize = length(subproblems); %initial the subproblem's initital state. inds = randompoint(mop, params.popsize); [V, INDS] = arrayfun(@evaluate, repmat(mop, size(inds)), inds, 'UniformOutput', 0); v = cell2mat(V); idealp = min(idealp, min(v,[],2)); %indcells = mat2cell(INDS, 1, ones(1,params.popsize)); [subproblems.curpoint] = INDS{:}; clear inds INDS V indcells; end function subproblems = evolve(subproblems, mop, params) global idealpoint; for i=1:length(subproblems) %new point generation using genetic operations, and evaluate it. ind = genetic_op(subproblems, i, mop.domain, params); [obj,ind] = evaluate(mop, ind); %update the idealpoint. idealpoint = min(idealpoint, obj); %update the neighbours. neighbourindex = subproblems(i).neighbour; subproblems(neighbourindex)=update(subproblems(neighbourindex),ind, idealpoint); %clear ind obj neighbourindex neighbours; clear ind obj neighbourindex; end end function subp =update(subp, ind, idealpoint) global params newobj=subobjective([subp.weight], ind.objective, idealpoint, params.dmethod); oops = [subp.curpoint]; oldobj=subobjective([subp.weight], [oops.objective], idealpoint, params.dmethod ); C = newobj < oldobj; [subp(C).curpoint]= deal(ind); clear C newobj oops oldobj; end function y =terminate(itrcounter) global params; y = itrcounter>params.iteration; end