粒子群算法(pso)标准测试函数验证程序

function PSOstandard_benchmarks_Test % 感谢亲亲使用此代码，此代码解决您的问题了吗~(@^_^@)~ % 没解决的话告诉亲亲一个好消息，登录淘宝店铺“大成软件工作室”，可以下载(????)1分钱成品代码(′▽`〃)哦~ % 是的，亲亲真的没有看错，挠破头皮的问题真的1分钱就可以解决了(づ??????)づ % 小的这就把传送门给您，记得要收藏好哦(づ￣3￣)づ╭?～ % 传送门：https://item.taobao.com/item.htm?spm=a1z10.1-c.w4004-15151018122.5.uwGoq5&id=538759553146 % 如果传送门失效，亲亲可以来店铺讨要，客服MM等亲亲来骚扰哦~(*/ω╲*) web https://item.taobao.com/item.htm?spm=a1z10.1-c.w4004-15151018122.5.uwGoq5&id=538759553146 -browser clear all; close all; c1=1.49445;c2=1.49445;% global dimension Size dimension=40;Size=40;%种群维数 dimension、规模 Size Tmax=1000;%%最大迭代次数 Tmax %%选择不同测试函数的速度和位置限制范围%% F_n=1; switch F_n case 1 %% f1_Sphere %% Vmax(1:dimension)= 30; Vmin(1:dimension)=-30; Xmax(1:dimension)= 30; Xmin(1:dimension)=-30; case 2 %% f2_Quadric [-100,100] %% Vmax(1:dimension)= 100; Vmin(1:dimension)=-100; Xmax(1:dimension)= 100; Xmin(1:dimension)=-100; case 3 %% f3_Ackley [-30,30] %% Vmax(1:dimension)= 30; Vmin(1:dimension)=-30; Xmax(1:dimension)= 30; Xmin(1:dimension)=-30; case 4 %% f4_griewank [-600,600] %% Vmax(1:dimension)= 600; Vmin(1:dimension)=-600; Xmax(1:dimension)= 600; Xmin(1:dimension)=-600; case 5 %% f5_Rastrigin [-5.12,5.12] %% Vmax(1:dimension)= 5.12; Vmin(1:dimension)=-5.12; Xmax(1:dimension)= 5.12; Xmin(1:dimension)=-5.12; case 6 %% f6_Rosenbrock [-2.408,2.408] %% Vmax(1:dimension)= 2.408; Vmin(1:dimension)=-2.408; Xmax(1:dimension)= 2.408; Xmin(1:dimension)=-2.408; case 7 %% f7_Schaffer's f6 %% Vmax(1:dimension)= 2.408; Vmin(1:dimension)=-2.408; Xmax(1:dimension)= 2.408; Xmin(1:dimension)=-2.408; end %%三维显示粒子群运动变化%% global Swarmscope; Swarmscope = plot(0,0, '.'); axis([Xmin(1) Xmax(1) Xmin(2) Xmax(2) Xmin(3) Xmax(3)]); %初始轴的范围的设置 % axis square; grid on; set(Swarmscope,'EraseMode','xor','MarkerSize',12); %设置用来显示粒子. %%initial Position Velocity%% Position=zeros(dimension,Size);%以后位置Position统一为此种记法：行 dimension；列 Size； Velocity=zeros(dimension,Size);%每个粒子的位置、速度对应于一列。 [Position,Velocity]=initial_Position_Velocity(dimension,Size,Xmax,Xmin,Vmax,Vmin); %%个体最优 P_p 和全局最优 globe 初始赋值%% P_p=Position;globe=zeros(dimension,1); %%评价每个粒子适应值，寻找出 globle%% for j=1:Size Pos=Position(:,j); fz(j)=Fitness_Function(Pos,F_n); end [P_g,I]=min(fz);%P_g 1*1 ? globe=Position(:,I); %%打散参数设置%% N_dismiss=51;%太小，不利于初始寻优 N_dismissed=0;%记录被打散的次数 deltaP_gg=0.001%种群过分收敛衡量标准值(适应度变化率) % reset = 1; %设置reset = 1时指示粒子群过分收敛时将被打散，如果reset＝0则不打散 reset_dismiss = 0; %%迭代开始%% for itrtn=1:Tmax time(itrtn)=itrtn; %%过于集中时打散%% if reset_dismiss==1 bit=1; if itrtn>N_dismiss bit=bit&((P_gg(itrtn-1)-P_gg(itrtn-N_dismiss))/P_gg(itrtn-1)< deltaP_gg); if bit==1 [Position,Velocity]=initial_Position_Velocity(dimension,Size,Xmax,Xmin,Vmax,Vmin);%重新初始化位置和速度 N_dismissed=N_dismissed+1; N_dismissed warning('粒子过分集中！重新初始化……'); % 给出信息 itrtn end end end Weight=0.4+0.5*(Tmax-itrtn)/Tmax; % Weight=1; r1=rand(1);r2=rand(1); for i=1:Size Velocity(:,i)=Weight*Velocity(:,i)+c1*r1*(P_p(:,i)-Position(:,i))+c2*r2*(globe-Position(:,i));%速度更新 end %%速度限制%% for i=1:Size %%引入速度边界变异%% % Vout_max=max(Velocity(:,i)); % Vout_min=min(Velocity(:,i)); % if Vout_max jj=1; K=ones(dimension,1); for row=1:dimension if Velocity(row,i)>Vmax(row) K(jj)=Vmax(row)/Velocity(row,i); jj=jj+1; elseif Velocity(row,i)<Vmin(row) K(jj)=Vmin(row)/Velocity(row,i); jj=jj+1; else end end Kmin=min(K); for row=1:dimension if Velocity(row,i)>Vmax(row) Velocity(row,i)=Velocity(row,i)*Kmin; elseif Velocity(row,i)<Vmin(row) Velocity(row,i)=Velocity(row,i)*Kmin; else end end end % K Position=Position+Velocity;%位置更新 %%位置限制%% for i=1:Size for row=1:dimension if Position(row,i)>Xmax(row) Position(row,i)=Xmax(row); elseif Position(row,i)<Xmin(row) Position(row,i)=Xmin(row); else end end end %%重新评价每个粒子适应值，更新个体最优 P_p 和全局最优 globe%% for j=1:Size xx=Position(:,j)'; fz1(j)=Fitness_Function(xx,F_n); if fz1(j)<fz(j) P_p(:,j)=Position(:,j); fz(j)=fz1(j); end % [P_g1,I]=min(fz1);%%%有改动 if fz1(j)<P_g P_g=fz1(j); % globe=Position(:,I); end end [P_g1 I]=min(fz); P_gg(itrtn)=P_g1; globe=P_p(:,I); % globe=Position(:,I); % itrtn % globe %% draw 粒子群运动变化图%% XX=Position(1,:);YY=Position(2,:);ZZ=Position(3,:); if dimension>= 3 set(Swarmscope,'XData',XX,'YData', YY, 'ZData', ZZ); elseif dimension== 2 set(Swarmscope,'XData',XX,'YData',YY );%设置 end xlabel('粒子第一维'); ylabel('粒子第二维'); zlabel('粒子第三维'); drawnow; end %%画‘评价值’变化曲线%% figure(1); BestFitness_plot(time,P_gg); %%画系统阶跃响应变化曲线%% % figure(2); % Step_2PID(globe) function BestFitness_plot(time,P_gg) plot(time,P_gg); xlabel('迭代的次数');ylabel('适应度值P_g'); function [Position,Velocity]=initial_Position_Velocity(dimension,Size,Xmax,Xmin,Vmax,Vmin) for i=1:dimension Position(i,:)=Xmin(i)+(Xmax(i)-Xmin(i))*rand(1,Size); Velocity(i,:)=Vmin(i)+(Vmax(i)-Vmin(i))*rand(1,Size); end function Fitness=Fitness_Function(X,F_n) global dimension Size % F_n 标准测试函数选择,其中: % n=1: f1_Sphere 测试 % n=2: f2_Quadric 测试 % n=3: f3_Ackley 测试 % n=4: f4_Griewank 测试 % n=5: f5_Rastrigin 测试 % n=6: f6_Rosenbrock 测试 % n=7: f7_Schaffer's f6 测试 注：此函数只接受两个变量，故dimension＝2。 switch F_n case 1 %% f1_Sphere %% Func_Rastrigin=X(:)'*X(:); Fitness=Func_Rastrigin; case 2 %% f2_Quadric %% res1=0; for row=1:dimension res1=res1+(sum(X(1:row)))^2; end Func_Quadric=res1; Fitness=Func_Quadric; case 3 %% f3_Ackley %% Func_Ackley=-20*exp(-0.2*sqrt((1/dimension)*(X(:)'*X(:))))-exp((1/dimension)*((cos(2*pi*X(:)')*cos(2*pi*X(:)))))+20+exp(1); Fitness=Func_Ackley; case 4 %% f4_griewank %% res1=X(:)'*X(:)/4000; res2=1; for row=1:dimension res2=res2*cos(X(row)/sqrt(row)); end Func_Griewank=res1-res2+1; Fitness=Func_Griewank; case 5 %% f5_Rastrigin %% Func_Rastrigin=X(:)'*X(:)-10*sum(cos(X(:)*2*pi))+10*dimension; Fitness=Func_Rastrigin; case 6 %% f6_Rosenbrock %% res1=0; for row=1:(dimension-1) res1=res1+100*(X(row+1)-X(row)^2)^2+(X(row)-1)^2; end Func_Rosenbrock=res1; Fitness=Func_Rosenbrock; case 7 %% f7_Schaffer's f6 %% Func_Sch