基于粒子群算法优化遥感图像阈值分割（代码完整，数据齐全）

function [T,result,bestpop]=PSOfun(fg) %% %计算P P=imhist(fg); Pt=cumsum(P); P=P/Pt(end); e=0.1*10^(-3); index=P==0; P(index)=e; Pt=cumsum(P); %% c1=1.5; c2=1.5; maxgen=300; popSize=20; popmax=255; popmin=0; Vmax=50; Vmin=-50; %% pop=round(255*rand(popSize,1)); V=zeros(popSize,1); fitness=FitFun(pop,P,Pt); result=zeros(maxgen,1); bestPop=ones(maxgen,1); %% %初始化 [bestFitness bestIndex]=max(fitness); zbest=pop(bestIndex); gbest=pop; fitnessgbest=fitness; fitnesszbest=bestFitness; index=1; load maydata.mat for ii=1:maxgen V=(V)*0.7+c1*rand(popSize,1).*(pop-gbest)+c2*randn(popSize,1).*(pop-zbest); V(V>Vmax)=Vmax; V(V<Vmin)=Vmin; pop=pop+0.7*V; pop(pop>popmax)=255*rand(); pop(pop<popmin)=255*rand(); fitness=FitFun(pop,P,Pt); % for j=1:popSize if fitness(j)>fitnessgbest(j) gbest(j,:)=pop(j,:); fitnessgbest(j)=fitness(j); end if fitness(j)>fitnesszbest zbest(:,1)=pop(j,1); fitnesszbest=fitness(j); index=j; bestpop = [bestpop pop(index)]; end end result(ii)=fitnesszbest; bestPop(ii)=pop(index); % if i>1 % line([i-1,i],[result(i-1),result(i)]); % end end % bestFitness=FitFun(bestPop); T=bestPop(end); % Best=bestPop; %% % figure(1),hold on % plot3(pop(:,1),pop(:,2),fitness,'o') % legend('Surface of the function','Initial solve','Final solve',0) % plot3(bestPop(:,1),bestPop(:,2),result,'o') % X1=ones(size(pop(:,2)))*pop(:,1)'; % Y1=pop(:,2)*ones(size(pop(:,1)')); % z1=sin(sqrt(X1.^2+Y1.^2))./sqrt(X1.^2+Y1.^2)+exp((cos(2*pi*X1)+cos(2*pi*Y1))./2)-2.71289; %