psolbg1.m

将PSO和LBG结合在一步迭代过程中

% =====================================================================
%                 PP algorithm 
%                 sub program for pp
%     Start Date: 15/07/2006
%   Last Changed: 21/08/2006
%          Usage: Before runing the program, make sure experimental image exist 
%  
%  Specification: The algorithm, proposed by Ji and Liao in 2006, incorporating
%                 both PSO algorithm and LBG to one iteration and using  
%                 particle-pair to explore in the problem space
%
% Copyright (c) Ji and Liao in 2006
% All rights Reserved
%==========================================================================

function [gbestnext,gbest_valuenext,t1,gbest_value]=PSOLBG1()
close all;
clear all;
%Initialization of PSO parameters
itmax=20; %Maximum iteration number
c1=0.3;
c2=0.5;
GL=255;
for iter=1:itmax
W(iter)=0.1;
end

%**********************************************************
IndexIteration=1;
D=16;
num_cluster=256;
GenTrainingVector;
tic;
%**********************************************************

%**********************************************************

%Initialization of positions of agents
a=0;  
b=255;
num_particle=2; % number of particle, should be more than
particle=zeros(num_particle,num_cluster*D,itmax); % particle is used to save all particle(num_particle)'s codebook(num_cluster*D) in all iteration(itmax)
% reshape codebook to one vector as a particle
particle(1,:,1)=reshape(z_cluster',1,size(z_cluster,1)*size(z_cluster,2));
for i=2:num_particle % initialize other particle as the 1st partile above 
    particle(i,:,:)=particle(1,:,:)/i;
end

%Initialization of velocities of agents
m=0;
n=255;
V=round(m+(n-m)*rand(num_particle,num_cluster*D,1));% produce velocity randomly for all particles and all itmax

%**********************************************************

fitness=zeros(num_particle,itmax);% each particle's fitness value for all iterations
pbest=zeros(num_particle,num_cluster*D,itmax); % each particle's position for all iteration
calfitness; %get each particle's fitness value in 1st iteration

%**********************************************************
pbest_value(:,1)=fitness(:,1);
[C,I]=min(abs(pbest_value(:,1)));
gbest_value(1)=C;
gbest(1,:,1)=particle(I,:,1);  % save the best particle 's position (in other words, the best codebook)

for i=1:num_particle; % for the 1st iteration, each particle's best position is themselves
       pbest(i,:,1)=particle(i,:,1);
end

% calculate psnr
Dis=gbest_value(1)/4096;
psnr = 10 * log10(255.^2*16 /Dis);

%******************************************************
while IndexIteration<=(itmax-1)
   IndexIteration=IndexIteration+1;



V(:,:,IndexIteration)=floor(W(IndexIteration)*V(:,:,IndexIteration-1)+c1*rand*(pbest(:,:,IndexIteration-1)...
    -particle(:,:,IndexIteration-1))+c2*rand*(repmat(gbest(:,:,IndexIteration-1),num_particle,1)-particle(:,:,IndexIteration-1)));
particle(:,:,IndexIteration)=particle(:,:,IndexIteration-1)+V(:,:,IndexIteration);

particle(:,:,IndexIteration)=min(particle(:,:,IndexIteration),255);

updateparticle_lhl;
calfitness;

%update pbest and pbest_value
pbest_value(:,IndexIteration)=min(pbest_value(:,IndexIteration-1),fitness(:,IndexIteration));
for (temp=1:num_particle)
    if pbest_value(temp,IndexIteration)== fitness(temp,IndexIteration)   
        pbest(temp,:,IndexIteration)=particle(temp,:,IndexIteration);
    else
        pbest(temp,:,IndexIteration)=pbest(temp,:,IndexIteration-1);
    end
end
% update gbest and gbest_value
[C,I]=min(abs(pbest_value(:,IndexIteration)));
gbest_value(IndexIteration)=min(C,gbest_value(IndexIteration-1));

    if gbest_value(IndexIteration)==C
        gbest(1,:,IndexIteration)=pbest(I,:,IndexIteration);  % save the best particle 's position (in other words, the best codebook)
    else
        gbest(1,:,IndexIteration)=gbest(1,:,IndexIteration-1);
    end
psnr(IndexIteration)=10 * log10(255.^2*16 /(gbest_value(1,IndexIteration)/4096));
% #########################################################################
% ########### add program to update pbest and gbest

end   % end for while
t1=toc;
% replace the tvs with corresponding codevectors
tempz_cluster=reshape(gbest(1,:,IndexIteration),D,num_cluster)';
for j=1:num_trainingvector 
    [C,I]=min(sum((repmat(trainingvector(j,:),num_cluster,1)-tempz_cluster).^2,2));
    new_tv(j,:)=tempz_cluster(I,:);
end

recn_img=img_lena;
for i=0:Line/q-1
   for j=0:Col/q-1
       recn_img(i*q+1,[j*q+1:j*q+q])=new_tv(i*Col/q+j+1,[1:4]);
       recn_img(i*q+2,[j*q+1:j*q+q])=new_tv(i*Col/q+j+1,[5:8]);
       recn_img(i*q+3,[j*q+1:j*q+q])=new_tv(i*Col/q+j+1,[9:12]);
       recn_img(i*q+4,[j*q+1:j*q+q])=new_tv(i*Col/q+j+1,[13:16]);
    end
end
[C,I]=find(recn_img<0);
[C1,I1]=find(recn_img>255);
GL=255;
mse=mean(mean((recn_img-img_lena).^2));
psnr;
psnr1=10*log10(GL*GL/mse);
%mse=mean(mean((recn_img-img_lena).^2));

figure;
imshow(recn_img/255);


%------------------------------------------------------------------
% for elitist particle-pair following
gbestnext=gbest(1,:,IndexIteration);
gbest_valuenext=gbest_value(IndexIteration);