【特征选择】基于教与学算法实现二进制特征选择问题附matlab代码

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智能优化算法  神经网络预测 雷达通信  无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机  电力系统

⛄ 内容介绍

简要分析了群智能优化算法的研究现状,重点对"教与学"优化算法作了详细的描述,并分析了"教与学"算法的性能及其优缺点;随后介绍了几种改进的"教与学"优化算法,对"教与学"优化算法的应用研究情况进行了论述。最后,说明了目前"教与学"优化算法中存在的问题,并指出"教与学"优化算法未来的研究方向。

⛄ 部分代码

%% TLBO + LPQ Image Feature Selection

% This code extract LPQ features out of 5 classes of images and selects best 

% desire number of features using TLBO algorithm. 

%% Making Things Ready !!!

clc;

clear; 

warning('off');

%% LPQ Feature Extraction

% Read input images

path='Dat';

fileinfo = dir(fullfile(path,'*.jpg'));

filesnumber=size(fileinfo);

for i = 1 : filesnumber(1,1)

images{i} = imread(fullfile(path,fileinfo(i).name));

disp(['Loading image No :   ' num2str(i) ]);

end;

% Color to Gray Conversion

for i = 1 : filesnumber(1,1)

images{i}=rgb2gray(images{i});

disp(['To Gray :   ' num2str(i) ]);end;

% Contrast Adjustment

for i = 1 : filesnumber(1,1)

adjusted2{i}=imadjust(images{i});

disp(['Contrast Adjust :   ' num2str(i) ]);end;

% Resize Image

for i = 1 : filesnumber(1,1)

resized2{i}=imresize(adjusted2{i}, [256 256]);

disp(['Image Resized :   ' num2str(i) ]);end;

%% LPQ Features

% clear LPQ_tmp;clear LPQ_Features;

winsize=9;

for i = 1 : filesnumber(1,1)

LPQ_tmp{i}=lpq(resized2{i},winsize);

disp(['Extract LPQ :   ' num2str(i) ]);end;

for i = 1 : filesnumber(1,1)

LPQ_Features(i,:)=LPQ_tmp{i};end;

%% Labeling for Classification

sizefinal=size(LPQ_Features);

sizefinal=sizefinal(1,2);

%

LPQ_Features(1:10,sizefinal+1)=1;

LPQ_Features(11:20,sizefinal+1)=2;

LPQ_Features(21:30,sizefinal+1)=3;

LPQ_Features(31:40,sizefinal+1)=4;

LPQ_Features(41:50,sizefinal+1)=5;

% ------------------------------------------------------

%% Feature Selection

% Data Preparation

x=LPQ_Features(:,1:end-1)';

t=LPQ_Features(:,end)';

data.x=x;

data.t=t;

data.nx=size(x,1);

data.nt=size(t,1);

data.nSample=size(x,2);

%% Number of Desired Features

nf=32;

%% Cost Function

CostFunction=@(u) FeatureCost(u,nf,data);

% Number of Decision Variables

nVar=data.nx;

% Size of Decision Variables Matrix

VarSize=[1 nVar];

% Lower Bound of Variables

VarMin=0;

% Upper Bound of Variables

VarMax=1;

%--------------------------------------------------------------

%% TLBO Parameters

MaxIt = 30;        

nPop = 2;          

empty_individual.Position = [];

empty_individual.Cost = [];

empty_individual.Out = [];

pop = repmat(empty_individual, nPop, 1);

BestSol.Cost = inf;

for i = 1:nPop

pop(i).Position = unifrnd(VarMin, VarMax, VarSize);

[pop(i).Cost pop(i).Out] = CostFunction(pop(i).Position);

if pop(i).Cost < BestSol.Cost

BestSol = pop(i);

end

end

BestCost = zeros(MaxIt, 1);

%% TLBO Body

for it = 1:MaxIt

Mean = 0;

for i = 1:nPop

Mean = Mean + pop(i).Position;

end

Mean = Mean/nPop;

% Select Teacher

Teacher = pop(1);

for i = 2:nPop

if pop(i).Cost < Teacher.Cost

Teacher = pop(i);

end

end

% Teacher 

for i = 1:nPop

newsol = empty_individual;

% Teaching Factor

TF = randi([1 2]);

% Teaching (moving towards teacher)

newsol.Position = pop(i).Position ...

+ rand(VarSize).*(Teacher.Position - TF*Mean);

% Clipping

newsol.Position = max(newsol.Position, VarMin);

newsol.Position = min(newsol.Position, VarMax);

% Evaluation

[newsol.Cost newsol.Out] = CostFunction(newsol.Position);

% Comparision

if newsol.Cost

pop(i) = newsol;

if pop(i).Cost < BestSol.Cost

BestSol = pop(i);

end

end

end

% Learner 

for i = 1:nPop

A = 1:nPop;

A(i) = [];

j = A(randi(nPop-1));

Step = pop(i).Position - pop(j).Position;

if pop(j).Cost < pop(i).Cost

Step = -Step;

end

newsol = empty_individual;

% Teaching (moving towards teacher)

newsol.Position = pop(i).Position + rand(VarSize).*Step;

% Clipping

newsol.Position = max(newsol.Position, VarMin);

newsol.Position = min(newsol.Position, VarMax);

% Evaluation

[newsol.Cost newsol.Out]= CostFunction(newsol.Position);

% Comparision

if newsol.Cost

pop(i) = newsol;

if pop(i).Cost < BestSol.Cost

BestSol = pop(i);

end

end

end

% Store Record for Current Iteration

BestCost(it) = BestSol.Cost;

disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);

end

% Plot ---------------------------------------------- 

plot(BestCost, '--k','linewidth',2);

xlabel('Iteration');

ylabel('Bees Cost');

%---------------------------------------------------

%% Creating Features Matrix

% Extracting Data

RealData=data.x';

% Extracting Labels

RealLbl=data.t';

FinalFeaturesInd=BestSol.Out.S;

% Sort Features

FFI=sort(FinalFeaturesInd);

% Select Final Features

Bio_Features=RealData(:,FFI);

% Adding Labels

Bio_Features_Lbl=Bio_Features;

Bio_Features_Lbl(:,end+1)=RealLbl;

LPQ_Bio=Bio_Features_Lbl;

% Plot

figure;

plot(FinalFeaturesInd, 'linewidth' , 2);

title('Selected Features');

xlabel ('Feature Number');

ylabel ('Feature Index');

⛄ 运行结果

⛄ 参考文献

[1]程亚维．基于教学与学习优化算法的糊糊性作业车辆间的调度问题[J]. 新乡学院学报, 2021, 38(9):6.

[2]杨文明,顾幸生. 基于混匀优化算法的连续生产过程重调度与库存优化[J]. 华东理工大学学报：自然科学版, 2006, 32(7):4.

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