智能优化算法程序代码集锦.docx
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智能优化算法程序代码集锦
人工蚂蚁算法
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function[x,y,minvalue]=AA（func）
%Example[x,y,minvalue]=AA（'Foxhole'）
clc;
tic;
subplot（2,2,1）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%plot1
draw（func）;
title（[func,'Function']）;
%初始化各参数
Ant=100;%蚂蚁规模
ECHO=200;%迭代次数
step=0.01*rand
（1）;%局部搜索时的步长
temp=[0,0];
%各子区间长度
start1=-100;
end1=100;
start2=-100;
end2=100;
Len1=（end1-start1）/Ant;
Len2=（end2-start2）/Ant;
%P=0.2;
%初始化蚂蚁位置
fori=1:
Ant
X（i,1）=（start1+（end1-start1）*rand
（1））;
X（i,2）=（start2+（end2-start2）*rand
（1））;
%func=AA_Foxhole_Func（X（i,1）,X（i,2））;
val=feval（func,[X（i,1）,X（i,2）]）;
T0（i）=exp（-val）;%初始信息素,随函数值大,信息素浓度小,反之亦然%%%%%*********************************************************************
end;
%至此初始化完成
forEcho=1:
ECHO%开始寻优
%P0函数定义,P0为全局转移选择因子
a1=0.9;
b1=（1/ECHO）*2*log（1/2）;
f1=a1*exp（b1*Echo）;
a2=0.225;
b2=（1/ECHO）*2*log
（2）;
f2=a2*exp（b2*Echo）;
ifEcho<=（ECHO/2）
P0=f1;
else
P0=f2;
end;
%P函数定义，P为信息素蒸发系数
a3=0.1;
b3=（1/ECHO）.*log（9）;
P=a3*exp（b3*Echo）;
lamda=0.10+（0.14-0.1）*rand
（1）;%全局转移步长参数
Wmax=1.0+（1.4-1.0）*rand
（1）;%步长更新参数上限
Wmin=0.2+（0.8-0.2）*rand
（1）;%步长更新参数下限
%寻找初始最优值
T_Best=T0
（1）;
forj=1:
Ant
ifT0（j）>=T_Best
T_Best=T0（j）;
BestIndex=j;
end;
end;
W=Wmax-（Wmax-Wmin）*（Echo/ECHO）;%局部搜索步长更新参数
forj_g=1:
Ant%全局转移概率求取,当该蚂蚁随在位置不是bestindex时
ifj_g~=BestIndex
r=T0（BestIndex）-T0（j_g）;
Prob（j_g）=exp（r）/exp（T0（BestIndex））;
else%当j_g=BestIndex的时候进行局部搜索
ifrand
（1）<0.5
temp（1,1）=X（BestIndex,1）+W*step;
temp（1,2）=X（BestIndex,2）+W*step;
else
temp（1,1）=X（BestIndex,1）-W*step;
temp（1,2）=X（BestIndex,2）-W*step;
end;
Prob（j_g）=0;%bestindex的蚂蚁不进行全局转移
end;
X1_T=temp（1,1）;
X2_T=temp（1,2）;
X1_B=X（BestIndex,1）;
X2_B=X（BestIndex,2）;
%func1=AA_Foxhole_Func（X1_T,X2_T）;%%%%%%%%%%%***************************************************
%F1_T=func1;
F1_T=feval（func,[X（i,1）,X（i,2）]）;
F1_B=feval（func,[X1_B,X2_B]）;
%F1_T=（X1_T-1）.^2+（X2_T-2.2）.^2+1;
%func2=AA_Foxhole_Func（X1_B,X2_B）;%%%%%%%%%%%%%***************************************************
%F1_B=func2;
%F1_B=（X1_B-1）.^2+（X2_B-2.2）.^2+1;
ifexp（-F1_T）>exp（-F1_B）
X（BestIndex,1）=temp（1,1）;
X（BestIndex,2）=temp（1,2）;
end;
end;
forj_g_tr=1:
Ant
ifProb（j_g_tr）X（j_g_tr,1）=X（j_g_tr,1）+lamda*（X（BestIndex,1）-X（j_g_tr,1））;%Xi=Xi+lamda*（Xbest-Xi）
X（j_g_tr,2）=X（j_g_tr,2）+lamda*（X（BestIndex,2）-X（j_g_tr,2））;%Xi=Xi+lamda*（Xbest-Xi）
X（j_g_tr,1）=bound（X（j_g_tr,1）,start1,end1）;
X（j_g_tr,2）=bound（X（j_g_tr,2）,start2,end2）;
else
X（j_g_tr,1）=X（j_g_tr,1）+（（-1）+2*rand
（1））*Len1;%Xi=Xi+rand（-1,1）*Len1
X（j_g_tr,2）=X（j_g_tr,2）+（（-1）+2*rand
（1））*Len2;%Xi=Xi+rand（-1,1）*Len2
X（j_g_tr,1）=bound（X（j_g_tr,1）,start1,end1）;
X（j_g_tr,2）=bound（X（j_g_tr,2）,start2,end2）;
end;
end;
%信息素更新
subplot（2,2,2）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot1
bar（[X（BestIndex,1）X（BestIndex,2）],0.25）;
%colormap（cool）;
axis（[03-4040]）;
title（{date;['Iteration',num2str（Echo）]}）;
xlabel（['Min_x=',num2str（X（BestIndex,1））,'','Min_y=',num2str（X（BestIndex,2））]）;
fort_t=1:
Ant
%func=AA_Foxhole_Func（X（t_t,1）,X（t_t,2））;
val1=feval（func,[X（t_t,1）,X（t_t,2）]）;
T0（t_t）=（1-P）*T0（t_t）+（exp（-val1））;%*************************************************************************
end;
[c_iter,i_iter]=max（T0）;%求取每代全局最优解
minpoint_iter=[X（i_iter,1）,X（i_iter,2）];
%func3=AA_Foxhole_Func（X（i_iter,1）,X（i_iter,2））;%%%%%%%%%***************************************************************************
val2=feval（func,[X（i_iter,1）,X（i_iter,2）]）;
minvalue_iter=val2;
%minvalue_iter=（X（i_iter,1）-1）.^2+（X（i_iter,2）-2.2）.^2+1;
min_local（Echo）=minvalue_iter;%保存每代局部最优解
subplot（2,2,3）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot2
plot（X（BestIndex,1）,X（BestIndex,2）,'rs','MarkerFaceColor','r','MarkerSize',8）,gridon;
title（['GlobalMinValue=',num2str（minvalue_iter）]）;
holdon;
plot（X（:
1）,X（:
2）,'g.'）,pause（0.02）;
holdoff;
axis（[-100100-100100]）;
gridon;
%将每代全局最优解存到min_global矩阵中
ifEcho>=2
ifmin_local（Echo）min_global（Echo）=min_local（Echo）;
else
min_global（Echo）=min_global（Echo-1）;
end;
else
min_global（Echo）=minvalue_iter;
end;
subplot（2,2,4）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot3
min_global=min_global';
index（:
1）=1:
ECHO;
plot（Echo,min_global（Echo）,'y*'）
%axis（[0ECHO010]）;
holdon;
title（[func,'（X）=',num2str（minvalue_iter）],'Color','r'）;
xlabel（'iteration'）;
ylabel（'f（x）'）;
gridon;
end;%ECHO循环结束
[c_max,i_max]=max（T0）;
minpoint=[X（i_max,1）,X（i_max,2）];
%func3=AA_Foxhole_Func（X（i_max,1）,X（i_max,2））;%%%*************************************************************************
%minvalue=func3;
minvalue=feval（func,[X（i_max,1）,X（i_max,2）]）;
x=X（BestIndex,1）;
y=X（BestIndex,2）;
runtime=toc
人工免疫算法
function[x,y,fx,vfx,vmfit,P,vpm]=AI（func,gen,n,pm,per）;
%Example[x,y,fx]=AI（'Foxhole'）
subplot（2,2,1）;
draw（func）;
title（[func,'Function']）;
ifnargin==1,
%gen=200;n=round（size（P,1）/2）;pm=0.0005;per=0.0;fat=10;
%gen=250;n=size（P,1）;pm=0.01;per=0.0;fat=.1;
P=cadeia（200,44,0,0,0）;
gen=40;n=size（P,1）;pm=0.2;per=0.0;fat=0.1;
end;
whilen<=0,
n=input（'nhastobeatleastone.Typeanewvalueforn:
'）;
end;
xmin=-100;
xmax=100;
ymin=-100;
ymax=100;
x=decode（P（:
1:
22）,xmin,xmax）;y=decode（P（:
23:
end）,ymin,ymax）;
%fit=eval（f）;
%fit=AI_Foxhole_Func（x,y）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fit=feval（func,[x'y']）;
%imprime（1,vxp,vyp,vzp,x,y,fit,1,1）;
%Hypermutationcontrollingparameters
pma=pm;itpm=gen;pmr=0.8;
%Generaldefintions
vpm=[];vfx=[];vmfit=[];valfx=1;
[N,L]=size（P）;it=0;PRINT=1;
%Generations
whileit<=gen&valfx<=100,
x=decode（P（:
1:
22）,xmin,xmax）;y=decode（P（:
23:
end）,ymin,ymax）;T=[];cs=[];
%fit=eval（f）;
%fit=AI_Foxhole_Func（x,y）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fit=feval（func,[x'y']）;
[a,ind]=sort（fit）;
valx=x（ind（end-n+1:
end））;valy=y（ind（end-n+1:
end））;
fx=a（end-n+1:
end）;%nbestindividuals（maximization）
%Reproduction
[T,pcs]=reprod（n,fat,N,ind,P,T）;
%Hypermutation
M=rand（size（T,1）,L）<=pm;
T=T-2.*（T.*M）+M;
T（pcs,:
）=P（fliplr（ind（end-n+1:
end））,:
）;
%NewRe-Selection（Multi-peaksolution）
x=decode（T（:
1:
22）,xmin,xmax）;y=decode（T（:
23:
end）,ymin,ymax）;
%fit=AI_Foxhole_Func（x,y）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fit=feval（func,[x'y']）;
%fit=eval（f）;
pcs=[0pcs];
fori=1:
n,
[out（i）,bcs（i）]=min（fit（pcs（i）+1:
pcs（i+1）））;%Mimimazionproblem%%%*************************
bcs（i）=bcs（i）+pcs（i）;
end;
P（fliplr（ind（end-n+1:
end））,:
）=T（bcs,:
）;
%Editing（Repertoireshift）
nedit=round（per*N）;it=it+1;
P（ind（1:
nedit）,:
）=cadeia（nedit,L,0,0,0）;
pm=pmcont（pm,pma,pmr,it,itpm）;valfx=min（fx）;%*************************************************************
vpm=[vpmpm];vfx=[vfxvalfx];vmfit=[vmfitmean（fit）];
disp（sprintf（'It.:
%dpm:
%.4fx:
%2.2fy:
%2.2fAv.:
%2.2ff（x,y）:
%2.3f',it,pm,valx
（1）,valy
（1）,vmfit
（1）,valfx））;
subplot（2,2,2）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot1
bar（[valx
（1）valy
（1）],0.25）;
axis（[03-4040]）;
title（['Iteration',num2str（it）]）;pause（0.1）;
subplot（2,2,3）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot2
plot（valx
（1）,valy
（1）,'rs','MarkerFaceColor','r','MarkerSize',8）
holdon;
%plot（x（:
1）,x（:
2）,'k.'）;
set（gca,'Color','g'）
holdoff;
gridon;
axis（[-100100-100100]）;
title（['GlobalMin=',num2str（valfx）]）;
xlabel（['Min_x=',num2str（valx
（1））,'Min_y=',num2str（valy
（1））]）;
subplot（2,2,4）;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot3
plot（it,valfx,'k.'）
axis（[0gen010]）;
holdon;
title（[func,'（X）=',num2str（valfx）]）;
xlabel（'iteration'）;
ylabel（'f（x）'）;
gridon;
end;%endwhile
%imprime（PRINT,vxp,vyp,vzp,x,y,fit,it,1）;
x=valx
（1）;y=valy
（1）;fx=min（fx）;%***********************************************************************
%x=P（ind（end）,1:
22）;y=P（ind（end）,23:
44）;fx=max（fx）;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot4
%---------------------%
%INTERNALSUBFUNCTIONS
%---------------------%
%Print
function[]=imprime（PRINT,vx,vy,vz,x,y,fx,it,mit）;
%x,fx->actualvalues
%vxplot,vplot->original（base）function
ifPRINT==1,
ifrem（it,mit）==0,
mesh（vx,vy,vz）;holdon;axis（[-100100-1001000500]）;
xlabel（'x'）;ylabel（'y'）;zlabel（'f（x,y）'）;
plot3（x,y,fx,'k*'）;drawnow;holdoff;
end;
end;
%Reproduction
function[T,pcs]=reprod（n,fat,N,ind,P,T）;
%n->numberofclones
%fat->multiplyingfactor
%ind->bestindividuals
%T->temporarypopulation
%pcs->finalpositionofeachclone
ifn==1,
cs=N;
T=ones（N,1）*P（ind
（1）,:
）;
else,
fori=1:
n,
%cs（i）=round（fat*N/i）;
cs（i）=round（fat*N）;
pcs（i）=sum（cs）;
T=[T;ones（cs（i）,1）*P（ind（end-i+1）,:
）];
end;
end;
%Controlofpm
function[pm]=pmcont（pm,pma,pmr,it,itpm）;
%pma->initialvalue
%pmr->controlrate
%itpm->iterationsforrestoring
ifrem（it,itpm）==0,
pm=pm*pmr;
ifrem（it,10*itpm）==0,
pm=pma;
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