溫馨提示×
您好，登錄后才能下訂單哦！
點擊 登錄注冊 即表示同意《億速云用戶服務條款》
您好，登錄后才能下訂單哦！
這篇文章主要介紹“matlab的TPO算法怎么用”的相關(guān)知識，小編通過實際案例向大家展示操作過程，操作方法簡單快捷，實用性強，希望這篇“matlab的TPO算法怎么用”文章能幫助大家解決問題。
%Tree Physiology Optimization (TPO) 無約束尋優(yōu)例程
function[Solution]=TPO_UnconstrainedOptim(n, Num_iterations)
tic;
if nargin<3, Num_iterations=30; end
if nargin<2, pop=30; end
if nargin<1, n=30; end
%目標函數(shù)開始（一個神奇的函數(shù)）
%--------------Shekel foxholes function---------------
strf1= '-((((x-9.681)^2+0.806)+ ((y-0.667)^2+0.806))^-1 + (((x-9.400)^2+0.517)+ ((y-2.041)^2+0.517))^-1 + (((x-8.025)^2+0.100)+ ((y-9.152)^2+0.100))^-1 + (((x-2.196)^2+0.908)+ ((y-0.415)^2+0.908))^-1 ';
strf2= '+(((x-8.074)^2+0.965)+ ((y-8.777)^2+0.965))^-1 + (((x-7.650)^2+0.669)+ ((y-5.658)^2+0.669))^-1 + (((x-1.256)^2+0.524)+ ((y-3.605)^2+0.524))^-1 + (((x-8.314)^2+0.902)+ ((y-2.261)^2+0.902))^-1 ';
strf3= '+(((x-0.226)^2+0.531)+ ((y-8.858)^2+0.531))^-1 + (((x-7.305)^2+0.876)+ ((y-2.228)^2+0.876))^-1 + (((x-0.652)^2+0.462)+ ((y-7.027)^2+0.462))^-1 +(((x-2.699)^2+0.491)+ ((y-3.516)^2+0.491))^-1 ';
strf4= '+(((x-8.327)^2+0.463)+ ((y-3.897)^2+0.463))^-1 + (((x-2.132)^2+0.714)+ ((y-7.006)^2+0.714))^-1 + (((x-4.707)^2+0.352)+ ((y-5.579)^2+0.352))^-1 + (((x-8.304)^2+0.869)+ ((y-7.559)^2+0.869))^-1';
strf5= '+(((x-8.632)^2+0.813)+ ((y-4.409)^2+0.813))^-1 + (((x-4.887)^2+0.811)+ ((y-9.112)^2+0.811))^-1 +(((x-2.440)^2+0.828)+ ((y-6.686)^2+0.828))^-1 + (((x-6.306)^2+0.964)+ ((y-8.583)^2+0.964))^-1';
strf6= '+(((x-0.652)^2+0.789)+ ((y-2.343)^2+0.789))^-1 + (((x-5.558)^2+0.360)+ ((y-1.272)^2+0.360))^-1 + (((x-3.352)^2+0.369)+ ((y-7.549)^2+0.369))^-1 + (((x-8.798)^2+0.992)+ ((y-0.880)^2+0.992))^-1';
strf7= '+(((x-1.460)^2+0.332)+ ((y-8.057)^2+0.332))^-1 + (((x-0.432)^2+0.817)+ ((y-8.645)^2+0.817))^-1 +(((x-0.679)^2+0.632)+ ((y-2.800)^2+0.632))^-1 + (((x-4.263)^2+0.883)+ ((y-1.074)^2+0.883))^-1';
strf8= '+ (((x-9.496)^2+0.608)+ ((y-4.830)^2+0.608))^-1 + (((x-4.138)^2+0.326)+ ((y-2.562)^2+0.326))^-1)';
funstr=strcat(strf1,strf2,strf3,strf4,strf5,strf6,strf7,strf8);
%目標函數(shù)終于結(jié)束了
% 把函數(shù)表達式字符串變成內(nèi)聯(lián)函數(shù)再矢量化
f=vectorize(inline(funstr));
range= [-10 10 -10 10];
%劃分網(wǎng)格
NumOfGrid=50;
deltgridx=(range(2)-range(1))/NumOfGrid;
deltgridy=(range(4)-range(3))/NumOfGrid;
x_grid=range(1):deltgridx:range(2); y_grid=range(3):deltgridy:range(4);
[x,y]= meshgrid(x_grid,y_grid);
z=f(x,y);
surfc(x,y,z);
%開始TPO算法
%初始化
[xn, yn, rnx, rny]=init_tree(pop,n);
figure(); grid on;
for jj=1:pop
zn(jj,:)=f(xn(jj,:),yn(jj,:)); %初始目標函數(shù)值
[z0(jj,1),Imin]=min(zn(jj,:)); %初始最優(yōu)值
x0(jj,1)=xn(jj,Imin);
y0(jj,1)=yn(jj,Imin);
r0x=rnx(jj,Imin);
r0y=rny(jj,Imin);
end
[xn, yn, rnx, rny]=init_tree(pop,n);
for jj=1:n
x00(:,jj)=x0; %初始枝干最優(yōu)
y00(:,jj)=y0;
end
z00=min(z0); %初始枝干最優(yōu)值
xn=xn+(x00-xn)+cos(pi/3).*5.*rand(pop,n); %植物生長
yn=yn+(y00-yn)+cos(pi/3).*5.*rand(pop,n);
[xn, yn,range]=range_tree(xn, yn, range,pop);
%主要迭代過程
countt=1; %記錄計數(shù)
for i=1:Num_iterations,
contour(x,y,z,15); %等高線圖
hold on;
%生長因數(shù)
%alpha = 樹根伸長的吸收常數(shù)
%beta = 樹枝的（經(jīng)驗表明30和70之間較為合適）.
%theta = 降低隨機化的因素，隨著迭代增加而減少隨機化的冪數(shù)
alpha=0.3;
beta= 50;
theta=0.9;
Theta=theta^(1*i);
%-----------------------------------------------------
zn=f(xn,yn);
for ij=1:pop
[zb(ij,1),Imin]=min(zn(ij,:)); %當前最優(yōu)
xb(ij,1)= xn(ij,Imin);
yb(ij,1)=yn(ij,Imin);
if zb(ij,1)<z0(ij) %和全局最優(yōu)比較
z0(ij)=zb(ij,1);
x0(ij)=xb(ij,1);
y0(ij)=yb(ij,1);
end
end
[z00,I00]=min(z0); %當前枝干最優(yōu)
for jj=1:n
x00(:,jj)=x0(I00); %當前整棵樹最優(yōu)
x0l(:,jj)=x0; %每一枝
y00(:,jj)=y0(I00);
y0l(:,jj)=y0;
end
Cx=Theta*(x0l-xn); %生成養(yǎng)分
Cy=Theta*(y0l-yn);
r0x=rnx; r0y=rny;
rnx=rnx+ (rand(pop,n)-0.5).*alpha.*Cx; %樹根生長
rny=rny+(rand(pop,n)-0.5).*alpha.*Cy;
Nx= Theta.* (rnx - r0x); %吸收養(yǎng)分
Ny= Theta.* (rny - r0y);
for ii=1:pop
xn=xn+(x00-xn)+(Nx).*cos(ii*pi/3).* beta; %樹枝生長
yn=yn+(y00-yn)+(Ny).*cos(ii*pi/3).* beta;
end
[xn, yn,range]=range_tree(xn, yn, range,pop);
[x00, y00,range]=range_tree(x00, y00, range,pop);
%每個樹枝達到最佳值
plot(xn, yn,'.',x0, y0, '*','MarkerSize',5); axis(range);
drawnow;
hold off;
Solution(countt,:)= [z00(1,1)];
countt=countt+1;
end
%初始化函數(shù)
function[xn, yn, rnx, rny]= init_tree(pop,n) %initialization value
xn=rand(pop,n);
yn=rand(pop,n);
rnx=rand(pop,n);
rny=rand(pop,n);
%檢測邊界
function[xn, yn, range] = range_tree(xn, yn, range,pop)
nn=length(xn);
for ij=1:nn
for iij=1:pop
if xn(iij,ij)<=range(1), xn(iij,ij)=range(1); end
if xn(iij,ij)>=range(2), xn(iij,ij)=range(2); end
if yn(iij,ij)<=range(3), yn(iij,ij)=range(3); end
if yn(iij,ij)>=range(4), yn(iij,ij)=range(4); end
end
關(guān)于“matlab的TPO算法怎么用”的內(nèi)容就介紹到這里了，感謝大家的閱讀。如果想了解更多行業(yè)相關(guān)的知識，可以關(guān)注億速云行業(yè)資訊頻道，小編每天都會為大家更新不同的知識點。
免責聲明：本站發(fā)布的內(nèi)容（圖片、視頻和文字）以原創(chuàng)、轉(zhuǎn)載和分享為主，文章觀點不代表本網(wǎng)站立場，如果涉及侵權(quán)請聯(lián)系站長郵箱：is@yisu.com進行舉報，并提供相關(guān)證據(jù)，一經(jīng)查實，將立刻刪除涉嫌侵權(quán)內(nèi)容。
億速云公眾號
手機網(wǎng)站二維碼
