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🏆部落客優勢：🌞🌞🌞部落格内容盡量做到思維缜密，邏輯清晰，為了友善讀者。
⛳️座右銘：行百裡者，半于九十。
目錄
💥1 概述
📚2 運作結果
🎉3 參考文獻
👨💻4 Matlab代碼實作
💥1 概述
當觀察周圍環境時,人們首先注意到的是物體及其周圍環境的顔色、紋理、形狀和空間關系等等,形狀是物體最基本的有感覺意義的特征之一.在計算機視覺和模式識别中,形狀是對目标範圍的二值圖像表示,可以看成是目标的輪廓,它是用于目辨別别的重要特征.為了節省存儲空間、易于特征計算﹐需要對形狀作進一步的表示,這些表示通常可以分為兩類:編碼方式.如鍊碼﹑遊程碼, freeman碼等;簡化方式,如樣條(B樣條,3次·5次樣條八插值、多項式、多邊形逼近、和特征點檢測等.另外還可以使用形狀的骨架來描述形狀。
📚2 運作結果
clear all
close all
clcglobal exp001
exp002 = imread('1.bmp');
exp001 = rgb2gray(exp002);
exp001 = edge(exp001,'sobel');global flag
global yanmoload zxx
[mmmm, ~] = size(yanmo);
flag = (mmmm./2);D = 3;
NP = 40; %/* The number of colony size (employed bees+onlooker bees)*/
FoodNumber = NP/2; %/*The number of food sources equals the half of the colony size*/
maxCycle = 50; %/*The number of cycles for foraging {a stopping criteria}*/
runtime = 3;%/*Algorithm can be run many times in order to see its robustness*/
limit = D*NP./2; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/
[zxx1,zxx2] = size(exp001);
lb = [flag+1 ,flag+1, 0]; %/*lower bounds of the parameters. */
ub = [zxx1-(flag + 1),zxx2- (flag + 1),2*pi];%/*upper bound of the parameters.*/GlobalMins=zeros(1,runtime);
Range = repmat((ub-lb),[FoodNumber 1]);
Lower = repmat(lb, [FoodNumber 1]); for r=1:runtime
tic
Foods = rand(FoodNumber,D) .* Range + Lower;
for i = 1:FoodNumber
ObjVal(i) = libai1989(Foods(i,1),Foods(i,2),Foods(i,3));
end
Fitness = calculateFitness(ObjVal);
trial=zeros(1,FoodNumber);
BestInd=find(ObjVal==max(ObjVal));
BestInd=BestInd(end);
GlobalMin = ObjVal(BestInd);
GlobalParams=Foods(BestInd,:);
ap = Fitness(1);iter=1;
while ((iter <= maxCycle)),
for i=1:(FoodNumber)
neighbour1 = fix(rand*(FoodNumber)) + 1;
neighbour2 = fix(rand*(FoodNumber)) + 1;
while(neighbour2 == i)
neighbour2 = fix(rand*(FoodNumber)) + 1;
end;
vv = (trial(i)+1)./((trial(i) + trial(neighbour2))+2);
sol=Foods(i,:);
Param2Change=fix(rand*D)+1;
sol(Param2Change) = Foods(i,Param2Change) + (Foods(neighbour1,Param2Change)-Foods(neighbour2,Param2Change))*(rand-0.5)*2*vv;
%
ind = find(sol<lb);
sol(ind) = lb(ind);
ind = find(sol>ub);
sol(ind) = ub(ind);
%
ObjValSol = libai1989(sol(1),sol(2),sol(3));
FitnessSol=calculateFitness(ObjValSol);
if (FitnessSol>Fitness(i))
Foods(i,:)=sol;
Fitness(i)=FitnessSol;
ObjVal(i)=ObjValSol;
trial(i)=0;
else
trial(i)=trial(i)+1;
end;
end;
prob=(0.9.*Fitness./max(Fitness))+0.1;
i=1;
t=0;
while(t<FoodNumber)
if(rand<prob(i))
t=t+1;
Param2Change=fix(rand*D)+1;
neighbour=fix(rand*(FoodNumber))+1;
while(neighbour==i)
neighbour=fix(rand*(FoodNumber))+1;
end;
sol=Foods(i,:);
vv = (trial(i) + 1) ./ ((trial(i) + trial(neighbour)) + 2);
sol(Param2Change) = Foods(i,Param2Change) + (Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2*vv;
%
ind = find(sol < lb);
sol(ind) = lb(ind);
ind = find(sol > ub);
sol(ind) = ub(ind);
%
ObjValSol = libai1989(sol(1),sol(2),sol(3));
FitnessSol=calculateFitness(ObjValSol);
if (FitnessSol<Fitness(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;
ind=find(ObjVal==max(ObjVal));
ind=ind(end);
if (ObjVal(ind)>GlobalMin)
GlobalMin=ObjVal(ind);
GlobalParams=Foods(ind,:);
end;
ind=find(trial==max(trial));
ind=ind(end);
if (trial(ind)>limit)
trial(ind)=0;
sol=(ub-lb).*rand(1,D)+lb;
ObjValSol = libai1989(sol(1),sol(2),sol(3));
FitnessSol=calculateFitness(ObjValSol);
Foods(ind,:)=sol;
Fitness(ind)=FitnessSol;
ObjVal(ind)=ObjValSol;
end;fprintf('iteration = %d ObjVal=%g\n',iter, GlobalMin);
AAA(r,iter) = GlobalMin;iter = iter +1;
end % End of ABC
solution_restore(r,1:3) = GlobalParams;
save BEend
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