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| %/* ABC algorithm */
clear all
close all
clc
%/* Control Parameters of ABC algorithm*/
NP=20; %/* The number of colony size (employed bees+onlooker bees)*/
FoodNumber=NP/2; %/*The number of food sources equals the half of the colony size*/
limit=100; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/
maxCycle=25; %/*The number of cycles for foraging {a stopping criteria}*/
%/* Problem specific variables*/
im1=imread('C:\Users\Aymen\Desktop\ABCmatlabcode\MATLABABCv2\image1.jpg');
im2=imread('C:\Users\Aymen\Desktop\ABCmatlabcode\MATLABABCv2\image4.jpg');
%[C1,C2]=census1(im1,im2);
voisinage=3;
C1 = TransCensus (im1,voisinage);
C2=TransCensus (im2,voisinage);
distance_hamming(C1 ,C2 );
objfun='distance_hamming'; %cost function to be optimized
D=10; %/*The number of parameters of the problem to be optimized*/
%ub=3;
%lb=-3;
ub=ones(1,D)*100; %/*lower bounds of the parameters. */
lb=ones(1,D)*(-100);%/*upper bound of the parameters.*/
runtime=1;%/*Algorithm can be run many times in order to see its robustness*/
%Foods [FoodNumber][D]; /*Foods is the population of food sources. Each row of Foods matrix is a vector holding D parameters to be optimized. The number of rows of Foods matrix equals to the FoodNumber*/
%ObjVal[FoodNumber]; /*f is a vector holding objective function values associated with food sources */
%Fitness[FoodNumber]; /*fitness is a vector holding fitness (quality) values associated with food sources*/
%trial[FoodNumber]; /*trial is a vector holding trial numbers through which solutions can not be improved*/
%prob[FoodNumber]; /*prob is a vector holding probabilities of food sources (solutions) to be chosen*/
%solution [D]; /*New solution (neighbour) produced by v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) j is a randomly chosen parameter and k is a randomlu chosen solution different from i*/
%ObjValSol; /*Objective function value of new solution*/
%FitnessSol; /*Fitness value of new solution*/
%neighbour, param2change; /*param2change corrresponds to j, neighbour corresponds to k in equation v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij})*/
%GlobalMin; /*Optimum solution obtained by ABC algorithm*/
%GlobalParams[D]; /*Parameters of the optimum solution*/
%GlobalMins[runtime]; /*GlobalMins holds the GlobalMin of each run in multiple runs*/
GlobalMins=zeros(1,runtime);
for r=1:runtime
% /*All food sources are initialized */
%/*Variables are initialized in the range [lb,ub]. If each parameter has different range, use arrays lb[j], ub[j] instead of lb and ub */
Range = repmat((ub-lb),[FoodNumber 1]);
Lower = repmat(lb, [FoodNumber 1]);
Foods = rand(FoodNumber,D) .* Range + Lower;
ObjVal=feval(objfun,Foods);
Fitness=calculateFitness(ObjVal);
%reset trial counters
trial=zeros(1,FoodNumber);
%/*The best food source is memorized*/
BestInd=find(ObjVal==min(ObjVal));
BestInd=BestInd(end);
GlobalMin=ObjVal(BestInd);
GlobalParams=Foods(BestInd,:);
iter=1;
while ((iter <= maxCycle)),
%%%%%%%%% EMPLOYED BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%
for i=1:(FoodNumber)
%/*The parameter to be changed is determined randomly*/
Param2Change=fix(rand*D)+1;
%/*A randomly chosen solution is used in producing a mutant solution of the solution i*/
neighbour=fix(rand*(FoodNumber))+1;
%/*Randomly selected solution must be different from the solution i*/
while(neighbour==i)
neighbour=fix(rand*(FoodNumber))+1;
end;
sol=Foods(i,:);
% /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;
% /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
ind=find(sol<lb);
sol(ind)=lb(ind);
ind=find(sol>ub);
sol(ind)=ub(ind);
%evaluate new solution
ObjValSol=feval(objfun,sol);
FitnessSol=calculateFitness(ObjValSol);
% /*a greedy selection is applied between the current solution i and its mutant*/
if (FitnessSol>Fitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/
Foods(i,:)=sol;
Fitness(i)=FitnessSol;
ObjVal(i)=ObjValSol;
trial(i)=0;
else
trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/
end;
end;
%%%%%%%%%%%%%%%%%%%%%%%% CalculateProbabilities %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%/* A food source is chosen with the probability which is proportioal to its quality*/
%/*Different schemes can be used to calculate the probability values*/
%/*For example prob(i)=fitness(i)/sum(fitness)*/
%/*or in a way used in the metot below prob(i)=a*fitness(i)/max(fitness)+b*/
%/*probability values are calculated by using fitness values and normalized by dividing maximum fitness value*/
prob=(0.9.*Fitness./max(Fitness))+0.1;
%%%%%%%%%%%%%%%%%%%%%%%% ONLOOKER BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
i=1;
t=0;
while(t<FoodNumber)
if(rand<prob(i))
t=t+1;
%/*The parameter to be changed is determined randomly*/
Param2Change=fix(rand*D)+1;
%/*A randomly chosen solution is used in producing a mutant solution of the solution i*/
neighbour=fix(rand*(FoodNumber))+1;
%/*Randomly selected solution must be different from the solution i*/
while(neighbour==i)
neighbour=fix(rand*(FoodNumber))+1;
end;
sol=Foods(i,:);
% /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;
% /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
ind=find(sol<lb);
sol(ind)=lb(ind);
ind=find(sol>ub);
sol(ind)=ub(ind);
%evaluate new solution
ObjValSol=feval(objfun,sol);
FitnessSol=calculateFitness(ObjValSol);
% /*a greedy selection is applied between the current solution i and its mutant*/
if (FitnessSol>Fitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/
Foods(i,:)=sol;
Fitness(i)=FitnessSol;
ObjVal(i)=ObjValSol;
trial(i)=0;
else
trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/
end;
end;
i=i+1;
if (i==(FoodNumber)+1)
i=1;
end;
end;
%/*The best food source is memorized*/
ind=find(ObjVal==min(ObjVal));
ind=ind(end);
if (ObjVal(ind)<GlobalMin)
GlobalMin=ObjVal(ind);
GlobalParams=Foods(ind,:);
end;
%%%%%%%%%%%% SCOUT BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%/*determine the food sources whose trial counter exceeds the "limit" value.
%In Basic ABC, only one scout is allowed to occur in each cycle*/
ind=find(trial==max(trial));
ind=ind(end);
if (trial(ind)>limit)
trial(ind)=0;
sol=(ub-lb).*rand(1,D)+lb;
ObjValSol=feval(objfun,sol);
FitnessSol=calculateFitness(ObjValSol);
Foods(ind,:)=sol;
Fitness(ind)=FitnessSol;
ObjVal(ind)=ObjValSol;
end;
fprintf('Ýter=%d ObjVal=%g\n',iter,GlobalMin);
iter=iter+1;
end % End of ABC
GlobalMins(r)=GlobalMin;
end; %end of runs
save all |
optimisation de fonction objectif (distance de hamming) par métaheuristique
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