【综合评价】基于粒子群算法优化投影寻踪模型的综合评价附matlab代码_[1]李学法, 周迎红. 基于粒子群优化算法的投影寻踪水质评价模型[j]. 湖北农业科学-优快云博客

本文链接：https://blog.youkuaiyun.com/matlab_dingdang/article/details/124972389

1 简介

利用投影寻踪法来评价水质,利用粒子群优化算法来求解最优函数问题,计算速度快,精度高.实例计算表明,基于粒子群优化算法寻找最优投影方向,方法简单,计算效率较高,收敛速度快,解决了投影寻踪过程中最优化问题,是解决复杂水质综合评价的有效途径.

2 部分代码

%function [gBest]=PSO()%PSO主函数    pso_size=100;%群体规模c1=0.5;c2=0.5;%学习因子w_max=0.8;%最大权重w_min=0.4;%最小权重w=w_max;Pb=10000000;%适应度值 Pb1=10000000;Pb2=10000000;dimens=1;%待优化问题的维数   run_max=100;%迭代次数上限X=zeros(1,pso_size);V=zeros(1,pso_size);Xb=zeros(1,pso_size);Xb1=zeros(1,pso_size);Xb2=zeros(1,pso_size);Yb=zeros(1,pso_size);Yb1=zeros(1,pso_size);Yb2=zeros(1,pso_size);P=zeros(1,8000);PL=zeros(1,pso_size);Pp=0;Pp1=0;Pp2=0;dpBest=zeros(1,pso_size);dgBest=0;gBest=0;s1=zeros(1,run_max);s2=zeros(1,run_max);q=0;L=0.05;%!!!!!!精度t=1;% !!!!!!!!!!P(t)中t初值为1for i=1:pso_size%初始化粒子速度位置    X(i)=-5+12*rand;    X1(i)=X(i);    X2(i)=X(i);    Xb(i)=X(i);    Xb1(i)=X(i);    Xb2(i)=X(i);    V(i)=(-12+24*rand)/10;endfor i=1:pso_size       Yb1(i)=f1(X(i));%计算f1中个体适应度值,步骤3    if Yb1(i)<Pb1        Pb1=Yb1(i);%f1中全局极值  步骤3,4        Pp1=X(i);    endendfor i=1:pso_size    Yb2(i)=f2(X(i));%计算f2中个体适应度值    if Yb2(i)<Pb2        Pb2=Yb2(i);%f2中全局极值    步骤3,4        Pp2=X(i);    endend  gBest=(Pp1+Pp2)/2;%  步骤5Pp=gBest;dgBest=abs(Pp1-Pp2);%计算全局最优值的距离for i=1:pso_size    dpBest(i)=abs(Xb1(i)-Xb2(i));%计算各粒子间的距离    if dpBest(i)<dgBest        q=rand;       if q>0.5           Xb(i)=Xb1(i);       else           Xb(i)=Xb2(i);       end %个体极值随机选取        else        Xb(i)=(Xb1(i)+Xb2(i))/2;%取两函数个体极值的均值    end    PL(i)=(Yb1(i)+Yb2(i))/2;%!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!    if PL(i)<L        P(t)=X(i);    t=t+1;    end%!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!endt%Pfor count=1:run_max     %循环迭代    for k=1:pso_size            V(k)=w*V(k)+c1*rand*(Xb(k)-X(k))+c2*rand*(Pp-X(k));            if abs(V(k))>12     %判断是否超出Vmax                if V(k)>0                    V(k)=12;                else                    V(k)=-12;                end            end             X(k)=X(k)+V(k);             X1(k)=X(k);             X2(k)=X(k);             temp1=f1(X1(k)); %$$$$在f1中计算个体适应度值$$$$             if temp1<Yb1(k)                 Yb1(k)=temp1;%$$$$判断是否更改个体极值$$$$                 Xb1(k)=X1(k);             end             if Yb1(k)<Pb1                 Pb1=Yb1(k);%$$$$得到新的f1中全局极值Pb1$$$$                 Pp1=X1(k);             end             temp2=f2(X2(k));  %$$$$在f2中计算个体适应度值$$$$             if temp2<Yb2(k)                 Yb2(k)=temp2;%$$$$判断是否更改个体极值$$$$                 Xb2(k)=X2(k);             end             if Yb2(k)<Pb2                 Pb2=Yb2(k);  %$$$$                 Pp2=X2(k);   %$$$$             end             gBest=(Pp1+Pp2)/2;    %得到全局最优              Pp=gBest;    %全局极值,             dgBest=abs(Pp1-Pp2);   %$$$$计算全局最优值的距离             dpBest(k)=abs(Xb1(k)-Xb2(k));             if dpBest(k)<dgBest    %$$$$比较个体极值的距离$$$$                 q=rand;             if q>0.5              Xb(k)=Xb1(k);            else             Xb(k)=Xb2(k);             end    %$$$$个体极值随机选取          else            Xb(k)=(Xb1(k)+Xb2(k))/2;   %$$$$取两函数个体极值的均值，得到目标均衡适应度值$$$$        end            PL(k)=(Yb1(k)+Yb2(k))/2;          %flag=0;        if PL(k)<L%!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!            a=0;            for e=1:t-1                if P(e)==X(k)                a=1;                end            end            if a~=1                    P(t)=X(k);                    t=t+1;            end        end     %   if PL(k)<L              %      for e=1:t       %         if P(t)不等于X(k)