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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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& {% J: s) |0 q- @7 Y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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伪代码
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0 I, \- l P) q1 m# M- 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; ?9 b& E" |: F3 G! ~/ `
: q d* X+ L4 i+ g+ B# [MATLAB实现:. _9 J% U0 l3 I5 d3 W
NSPSO:2 t& M" G9 m! N9 s1 J# b9 D$ y g) p
; Q+ Y3 N6 @8 {5 W6 y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
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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
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+ T. B9 E9 {$ W, Q; S: r; v4 J5 O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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8 I5 G9 A$ H9 r- Z- e9 f- b: Q Truncation.m代码:6 k4 s7 W9 y X$ [8 X
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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
- end8 ?, i; Z m' s" a
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