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#技术风云榜#基于非支配排序的多目标PSO算法MATLAB实现

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发表于 2020-11-12 13:35 | 只看该作者 |只看大图 回帖奖励 |倒序浏览 |阅读模式

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#技术风云榜#基于非支配排序的多目标PSO算法MATLAB实现, f( L; _, [; W" o+ {$ \1 S1 Z

$ r% e8 |& g- q" |' R, o, p) S% r这一篇是Xue Bing在一区cybernetics发的论文,里面提出了两个多目标PSO特征选择算法,一个是NSPSO另一个是CMDPSO。
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具体流程' B4 ^* i' j7 y+ B
  • ①划分数据集为测试集和训练集
  • ②初始化PSO算法
  • ③迭代开始
  • ④计算两个目标值(论文中是特征数和错误率)
  • ⑤非支配排序
  • ⑥拥挤距离度量并排序
  • ⑥对每个粒子从第一前沿面选择一个粒子作为gbest,更新当前粒子
  • ⑦调整粒子群
  • ⑧迭代结束返回
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2 `0 |! {! f% f& _MATLAB实现:
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! c' y6 h6 b% e- _注意其中FSKNN是我的问题的评价函数,包含两个目标值,都存入到pfitness中% B5 y  H8 |& F) Q9 W( j

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  • function [solution,time,pop,pfitness,site,LeaderAVE] = NSPSO(train_F,train_L)
  • tic
  • global maxFES
  • dim = size(train_F,2);
  • FES = 1;
  • sizep = 30;
  • pop = rand(sizep,dim);
  • popv = rand(sizep,dim);
  • pfitness = zeros(sizep,2);
  • LeaderAVE = zeros(1,2);
  • while FES <maxFES
  •     Off_P = zeros(sizep,dim);
  •     Off_V = zeros(sizep,dim);
  •     ofitness = zeros(sizep,2);
  •     for i=1:sizep
  •         [pfitness(i,1),pfitness(i,2)] = FSKNN(pop(i,:),i,train_F,train_L);
  •     end
  •     Front = NDSort(pfitness(:,1:2),sizep);
  •     [~,rank] = sortrows([Front',-CrowdingDistance(pfitness,Front)']);
  •     LeaderSet = rank(1:10);
  •     solution = pfitness(LeaderSet,:);
  •     LeaderAVE(1) = mean(solution(:,1));
  •     LeaderAVE(2) = mean(solution(:,2));
  •     for i = 1:sizep
  •         good = LeaderSet(randperm(length(LeaderSet),1));
  •         r1 = rand(1,dim);
  •         r2 = rand(1,dim);
  •         Off_V(i,:) = r1.*popv(i,:) +  r2.*(pop(good,:)-pop(i,:));
  •         Off_P(i,:) = pop(i,:) + Off_V(i,:);
  •     end
  •     for i=1:sizep
  •             [ofitness(i,1),ofitness(i,2)] = FSKNN(Off_P(i,:),i,train_F,train_L);
  •     end
  •     temppop = [pop;Off_P];
  •     tempv = [popv;Off_V];
  •     tempfiness = [pfitness;ofitness];
  •     [FrontNO,MaxFNO] = NDSort(tempfiness(:,1:2),sizep);
  •     Next = false(1,length(FrontNO));
  •     Next(FrontNO<MaxFNO) = true;
  •     PopObj = tempfiness;
  •     fmax   = max(PopObj(FrontNO==1,:),[],1);
  •     fmin   = min(PopObj(FrontNO==1,:),[],1);
  •     PopObj = (PopObj-repmat(fmin,size(PopObj,1),1))./repmat(fmax-fmin,size(PopObj,1),1);
  •     % Select the solutions in the last front
  •     Last = find(FrontNO==MaxFNO);
  •     del  = Truncation(PopObj(Last,:),length(Last)-sizep+sum(Next));
  •     Next(Last(~del)) = true;
  •     % Population for next generation
  •     pop = temppop(Next,:);
  •     popv = tempv(Next,:);
  •     pfitness = tempfiness(Next,:);
  •     fprintf('GEN: %2d   Error: %.4f  F:%.2f\n',FES,LeaderAVE(1),LeaderAVE(2));
  •     FES = FES + 1;
  • end
  • [FrontNO,~] = NDSort(pfitness(:,1:2),sizep);
  • site = find(FrontNO==1);
  • solution = pfitness(site,:);
  • LeaderAVE(1) = mean(solution(:,1));
  • LeaderAVE(2) = mean(solution(:,2));
  • toc
  • time = toc;
  • end; |% u8 }9 w* @1 L
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. g( h# R8 I' m+ ~$ t6 {  P/ s拥挤距离代码:
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  • function CrowdDis = CrowdingDistance(PopObj,FrontNO)
  • % Calculate the crowding distance of each solution front by front
  • % Copyright 2015-2016 Ye Tian
  •     [N,M]    = size(PopObj);
  •     CrowdDis = zeros(1,N);
  •     Fronts   = setdiff(unique(FrontNO),inf);
  •     for f = 1 : length(Fronts)
  •         Front = find(FrontNO==Fronts(f));
  •         Fmax  = max(PopObj(Front,:),[],1);
  •         Fmin  = min(PopObj(Front,:),[],1);
  •         for i = 1 : M
  •             [~,Rank] = sortrows(PopObj(Front,i));
  •             CrowdDis(Front(Rank(1)))   = inf;
  •             CrowdDis(Front(Rank(end))) = inf;
  •             for j = 2 : length(Front)-1
  •                 CrowdDis(Front(Rank(j))) = CrowdDis(Front(Rank(j)))+(PopObj(Front(Rank(j+1)),i)-PopObj(Front(Rank(j-1)),i))/(Fmax(i)-Fmin(i));
  •             end
  •         end
  •     end
  • end
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1 n9 l2 Y) [" u* l4 T    Truncation.m代码:" V" [# _6 T- ~& z: x' l
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  • function Del = Truncation(PopObj,K)
  • % Select part of the solutions by truncation
  •     N = size(PopObj,1);
  •     %% Truncation
  •     Distance = pdist2(PopObj,PopObj);
  •     Distance(logical(eye(length(Distance)))) = inf;
  •     Del = false(1,N);
  •     while sum(Del) < K
  •         Remain   = find(~Del);
  •         Temp     = sort(Distance(Remain,Remain),2);
  •         [~,Rank] = sortrows(Temp);
  •         Del(Remain(Rank(1))) = true;
  •     end
  • end/ w! e6 @. m* J  N. a

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发表于 2020-11-12 14:19 | 只看该作者
基于非支配排序的多目标PSO算法MATLAB实现
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