智能优化算法程序代码集锦Word文档下载推荐.docx
《智能优化算法程序代码集锦Word文档下载推荐.docx》由会员分享，可在线阅读，更多相关《智能优化算法程序代码集锦Word文档下载推荐.docx（95页珍藏版）》请在冰豆网上搜索。
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;
%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:
ifT0（j）>
=T_Best
T_Best=T0（j）;
BestIndex=j;
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;
Prob（j_g）=0;
%bestindex的蚂蚁不进行全局转移
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）;
forj_g_tr=1:
ifProb（j_g_tr）<
P0
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））;
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）;
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
%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:
%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））;
%*************************************************************************
[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-1）
min_global（Echo）=min_local（Echo）;
min_global（Echo）=min_global（Echo-1）;
min_global（Echo）=minvalue_iter;
subplot（2,2,4）;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot3
min_global=min_global'
;
index（:
1）=1:
ECHO;
plot（Echo,min_global（Echo）,'
y*'
%axis（[0ECHO010]）;
title（[func,'
（X）='
num2str（minvalue_iter）],'
Color'
）;
xlabel（'
iteration'
ylabel（'
f（x）'
%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（'
title（[func,'
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;
fat=.1;
P=cadeia（200,44,0,0,0）;
gen=40;
pm=0.2;
fat=0.1;
whilen<
=0,
n=input（'
nhastobeatleastone.Typeanewvalueforn:
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（:
T=[];
cs=[];
%fit=eval（f）;
%fit=AI_Foxhole_Func（x,y）;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fit=feval（func,[x'
[a,ind]=sort（fit）;
valx=x（ind（end-n+1:
end））;
valy=y（ind（end-n+1:
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（:
y=decode（T（:
pcs=[0pcs];
fori=1:
n,
[out（i）,bcs（i）]=min（fit（pcs（i）+1:
pcs（i+1）））;
%Mimimazionproblem%%%*************************
bcs（i）=bcs（i）+pcs（i）;
P（fliplr（ind（end-n+1:
）=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）;
num2str（it）]）;
pause（0.1）;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot2
plot（valx
（1）,valy
（1）,'
8）
%plot（x（:
1）,x（:
k.'
set（gca,'
g'
holdoff;
axis（[-100100-100100]）;
title（['
GlobalMin='
num2str（valfx）]）;
xlabel（['
Min_x='
num2str（valx
（1））,'
Min_y='
num2str（valy
（1））]）;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Plot3
plot（it,valfx,'
axis（[0gen010]）;
title（[func,'
（X）='
num2str（valfx）]）;
%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）;
axis（[-100100-1001000500]）;
x'
y'
zlabel（'
f（x,y）'
plot3（x,y,fx,'
k*'
drawnow;
%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,
%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）,:
）];
%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;
升级会员 