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人工蜂群算法部分
%人工蜂群算法部分
%experiment.m
%人工蜂群算法第一次应用
clear all %preprocessing预处理
close all
clc
%图像预处理
%I=imread(‘E:\lena512.bmp‘);
%R=imread(‘E:\car.bmp‘);
%R=imread(‘E:\bird.jpg‘);
%I=imread(‘AT3_1m4_01.tif‘);
%I=imread(‘rice.png‘);
R=imread(‘peppers.png‘);
I=rgb2gray(R);
%二维直方图平面分布图
row=size(I,1);
column=size(I,2);
N0=row*column;
M=zeros(256,256);%区域平均灰度值图像
for i=1:row
for j=1:column
k=0;
for i1=-1:1
for i2=-1:1
try %边缘区域处理
k=k+I(i+i1,j+i2)/9;
catch
%k=k;
end
end
end%邻域平均值计算
if (I(i,j)==0||k==0)
else
M(I(i,j),k)=M(I(i,j),k)+1;%记录用以生成像素周边区域平均像素
M1(i,j)=k;
end
end %图像循环
end
%ui的计算
sum0=0;
for s1=1:256
for t1=1:256
sum0=sum0+M(s1,t1)*s1;
end
end
ui=sum0/N0;
%uj的计算
sum0=0;
for s1=1:256
for t1=1:256
sum0=sum0+M(s1,t1)*t1;
end
end
uj=sum0/N0;
u=[ui,uj];
%人工蜂群算法部分
NP=50; %蜂群规模
FoodNumber=NP/2; %食物源数量
limit=100; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/蜂群规模*维数比较合适
maxCycle=600; %觅食周期数--循环次数
%参数定义
objfun=‘DDOTSU‘; %cost function to be optimized 成本函数
D=4; %/*The number of parameters of the problem to be optimized 要优化参数数量*/
ub=ones(1,D)*255; %/*lower bounds of the parameters. 参数下界*/
lb=ones(1,D)*(1);%/*upper bound of the parameters.*/
runtime=1;%/*Algorithm can be run many times in order to see its robustness*/鲁棒性检测
GlobalMins=zeros(1,runtime);
for r=1:runtime
% /*All food sources are initialized */解空间生成
%/*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;
Foods=round(Foods);
ObjVal=feval(objfun,Foods,M,N0,u);
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 雇佣蜂%%%%%%%%%%%%%%%%%%%%%%%%
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;
sol=round(sol);
% /*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,M,N0,u);
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;
sol=round(sol);
% /*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,M,N0,u);
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)
Bas(ind)=0;
sol=(ub-lb).*rand(1,D)+lb;
sol=round(sol);
ObjValSol=feval(objfun,sol,M,N0,u);
FitnessSol=calculateFitness(ObjValSol);
Foods(ind,:)=sol;
Fitness(ind)=FitnessSol;
ObjVal(ind)=ObjValSol;
end;
%fprintf(‘iter=%d ObjVal=%g\n‘,iter,GlobalMin);
iter=iter+1;
end % End of ABC
GlobalMins(r)=GlobalMin;
end; %end of runs
%fprintf(‘pos=%g\n‘,mean(pos,2));
%fprintf(‘mean=%g\n‘,mean(GlobalMins));
save all
%人工蜂群算法部分结束
BestValue=[(GlobalParams(1)+GlobalParams(3))/2 (GlobalParams(2)+GlobalParams(4))/2];
if BestValue(1)>BestValue(2)
cach=BestValue(1);
BestValue(1)=BestValue(2);
BestValue(2)=cach;
end
%根据阈值进行图像分割
for i=1:row
for j=1:column
if(I(i,j)>=BestValue(1)&&I(i,j)<=BestValue(2))
K(i,j)=1;
else
K(i,j)=0;
end
end
end
figure;
subplot(121);imshow(I);
subplot(122);imshow(K);
二维双阈值算法部分
%DDOTSU.m
function ObjVal = DDOTSU(Chrom,M,N0,u)
FoodNumber=size(Chrom,1);
ObjVal=zeros(1,FoodNumber);
for fn=1:FoodNumber
s1=Chrom(fn,1);
s2=Chrom(fn,2);
t1=Chrom(fn,3);
t2=Chrom(fn,4);
%%
%w0的计算
sum0=0;
for s=s1:s2
for t=t1:t2
sum0=sum0+M(s,t);
end
end
w0=sum0/N0;
%w1的计算
sum0=0;
for s=1:s1
for t=1:t2
sum0=sum0+M(s,t);
end
end
w1=sum0/N0;
sum0=0;
for s=s2:256
for t=t2:256
sum0=sum0+M(s,t);
end
end
w2=sum0/N0;
%%
%ui0的计算
sum0=0;
for s=s1:s2
for t=t1:t2
sum0=sum0+M(s,t)*s;
end
end
ui0=sum0/N0;
%uj0的计算
sum0=0;
for s=s1:s2
for t=t1:t2
sum0=sum0+M(s,t)*t;
end
end
uj0=sum0/N0;
u0=[ui0,uj0];
%%
%ui1的计算
sum0=0;
for s=1:s1
for t=1:t1
sum0=sum0+M(s,t)*s;
end
end
ui1=sum0/N0;
sum0=0;
for s=s2:256
for t=t2:256
sum0=sum0+M(s,t)*s;
end
end
ui2=sum0/N0;
%uj1的计算
sum0=0;
for s=1:s1
for t=1:t1
sum0=sum0+M(s,t)*t;
end
end
uj1=sum0/N0;
sum0=0;
for s=s2:256
for t=t2:256
sum0=sum0+M(s,t)*t;
end
end
uj2=sum0/N0;
u1=[ui1,uj1];
u2=[ui2,uj2];
%%
trsb=w0*(u0-u)*(u0-u)‘+w1*(u1-u)*(u1-u)‘+w2*(u2-u)*(u2-u)‘;
ObjVal(fn)=1/trsb;
end
end
评估函数
%calculateFitness.m
function fFitness=calculateFitness(fObjV)
fFitness=zeros(size(fObjV));
ind=find(fObjV>=0);
fFitness(ind)=1./(fObjV(ind)+1);
ind=find(fObjV<0);
fFitness(ind)=1+abs(fObjV(ind));
原文：https://www.cnblogs.com/zhoushuaiyi/p/14670703.html