动态算术优化算法 (DAOA)附matlab代码

%__________________________________________________________________ % % % % Dynamic Arithmetic Optimization Algorithm (DAOA) % % % %% Function Details % function [Best_FF,Best_P,Conv_curve]=DAOA(N,M_Iter,LB ,UB,Dim,ObjFuncName) display('AOA Working'); Dim=2; % Number of Variable LB = (0)*ones(1,Dim); % Upper Bound UB = (1)*ones(1,Dim); % Lower Bound F_obj= @F1; %Name of the Function N=5; %Number of Colors (Npop) M_Iter=500; Best_P=zeros(1,Dim); Best_FF=inf; Conv_curve=zeros(1,M_Iter); %% Initialize the positions of solution X=initialization(N,Dim,UB,LB); Xnew=X; Ffun=zeros(1,size(X,1)); % (fitness values) Ffun_new=zeros(1,size(Xnew,1)); % (fitness values) C_Iter=1; Mu=0.001; alpha=25; for i=1:size(X,1) Ffun(1,i)=F_obj(X(i,:)); %Calculate the fitness values of solutions if Ffun(1,i)<Best_FF Best_FF=Ffun(1,i); Best_P=X(i,:); end end %% Main Loop while C_Iter<M_Iter+1 % Main loop %DAF=((C_Iter/M_Iter+1)^((-1)*alpha)); %DAF1 DAF=(M_Iter+1/C_Iter)^((alpha)); %DAF2 DCS=.99*((1-(C_Iter/M_Iter)^(0.5))); % DCS %Upate the Position of solutions for i=1:size(X,1) % if each of the UB and LB has a just value for j=1:size(X,2) r1=rand(); if (size(LB,2)==1) if r1<DAF r2=rand(); if r2>0.5 Xnew(i,j)=(Best_P(1,j)/(DCS+eps)*((UB-LB)*Mu+LB)); else Xnew(i,j)=(Best_P(1,j)*DCS*((UB-LB)*Mu+LB)); end else r3=rand(); if r3>0.5 Xnew(i,j)=(Best_P(1,j)-DCS*((UB-LB)*Mu+LB)); else Xnew(i,j)=(Best_P(1,j)+DCS*((UB-LB)*Mu+LB)); end end end if (size(LB,2)~=1) % if each of the UB and LB has more than one value r1=rand(); if r1<DAF r2=rand(); if r2>0.5 Xnew(i,j)=((Best_P(1,j)/(DCS+eps)*((UB(j)-LB(j))*Mu+LB(j)))); else Xnew(i,j)=((Best_P(1,j)*DCS*((UB(j)-LB(j))*Mu+LB(j)))); end else r3=rand(); if r3>0.5 Xnew(i,j)=((Best_P(1,j)-DCS*((UB(j)-LB(j))*Mu+LB(j)))); else Xnew(i,j)=((Best_P(1,j)+DCS*((UB(j)-LB(j))*Mu+LB(j)))); end end end end Flag_UB=Xnew(i,:)>UB; % check if they exceed (up) the boundaries Flag_LB=Xnew(i,:)<LB; % check if they exceed (down) the boundaries Xnew(i,:)=(Xnew(i,:).*(~(Flag_UB+Flag_LB)))+UB.*Flag_UB+LB.*Flag_LB; Ffun_new(1,i)=F_obj(Xnew(i,:)); % calculate Fitness function if Ffun_new(1,i)<Ffun(1,i) X(i,:)=Xnew(i,:); Ffun(1,i)=Ffun_new(1,i); end if Ffun(1,i)<Best_FF Best_FF=Ffun(1,i); Best_P=X(i,:); end end %Update the convergence curve figure Conv_curve(C_Iter)=Best_FF; plot(Conv_curve,'Color','r','LineWidth',2) title('Convergence curve') xlabel('Iteration'); ylabel('Best fitness function'); axis tight legend('DAOA') %Print the best solution details after every 50 iterations % if mod(C_Iter,50)==0 display(['At iteration ', num2str(C_Iter), ' the best solution fitness is ', num2str(Best_FF)]); % end C_Iter=C_Iter+1; % incremental iteration end