KH磷虾群优化算法的MATLAB的仿真-源码

clc; clear; close all; warning off; addpath(genpath(pwd)); format long %% Initial Parameter Setting NR = 10; % Number if Runs NK = 25; % Number if Krills MI = 200; % Maximum Iteration C_flag = 1; % Crossover flag [Yes=1] % Bounds (Normalize search space in case of highly imbalanced search space) UB = 10*ones(1,10); LB = -10*ones(1,10); NP = length(LB); % Number if Parameter(s) Dt = mean(abs(UB-LB))/2; % Scale Factor F = zeros(NP,NK);D = zeros(1,NK);N = zeros(NP,NK); %R = zeros(NP,NK); Vf = 0.02; Dmax = 0.005; Nmax = 0.01; Sr = 0; %% Optimization & Simulation for nr = 1:NR %Initial Krills positions for z1 = 1:NP X(z1,:) = LB(z1) + (UB(z1) - LB(z1)).*rand(1,NK); end for z2 = 1:NK K(z2)=cost(X(:,z2)); end Kib=K; Xib=X; [Kgb(1,nr), A] = min(K); Xgb(:,1,nr) = X(:,A); for j = 1:MI % Virtual Food for ll = 1:NP; Sf(ll) = (sum(X(ll,:)./K)); end Xf(:,j) = Sf./(sum(1./K)); %Food Location Xf(:,j) =findlimits(Xf(:,j)',LB,UB,Xgb(:,j,nr)');% Bounds Checking Kf(j) = cost(Xf(:,j)); if 2<=j if Kf(j-1)<Kf(j) Xf(:,j) = Xf(:,j-1); Kf(j) = Kf(j-1); end end Kw_Kgb = max(K)-Kgb(j,nr); w = (0.1+0.8*(1-j/MI)); for i = 1:NK % Calculation of distances Rf = Xf(:,j)-X(:,i); Rgb = Xgb(:,j,nr)-X(:,i); for ii = 1:NK RR(:,ii) = X(:,ii)-X(:,i); end R = sqrt(sum(RR.*RR)); % % % % % % % % % % % % % Movement Induced % % % % % % % % % % % Calculation of BEST KRILL effect if Kgb(j,nr) < K(i) alpha_b = -2*(1+rand*(j/MI))*(Kgb(j,nr) - K(i)) /Kw_Kgb/ sqrt(sum(Rgb.*Rgb)) * Rgb; else alpha_b=0; end % Calculation of NEIGHBORS KRILL effect nn=0; ds = mean(R)/5; alpha_n = 0; for n=1:NK if and(R<ds,n~=i) nn=nn+1; if and(nn<=4,K(i)~=K(n)) alpha_n = alpha_n-(K(n) - K(i)) /Kw_Kgb/ R(n) * RR(:,n); end end end % Movement Induced N(:,i) = w*N(:,i)+Nmax*(alpha_b+alpha_n); % % % % % % % % % % % % % Foraging Motion % % % % % % % % % % % Calculation of FOOD attraction if Kf(j) < K(i) Beta_f=-2*(1-j/MI)*(Kf(j) - K(i)) /Kw_Kgb/ sqrt(sum(Rf.*Rf)) * Rf; else Beta_f=0; end % Calculation of BEST psition attraction Rib = Xib(:,i)-X(:,i); if Kib(i) < K(i) Beta_b=-(Kib(i) - K(i)) /Kw_Kgb/ sqrt(sum(Rib.*Rib)) *Rib; else Beta_b=0; end % Foraging Motion F(:,i) = w*F(:,i)+Vf*(Beta_b+Beta_f); % % % % % % % % % % % % % Physical Diffusion % % % % % % % % % D = Dmax*(1-j/MI)*floor(rand+(K(i)-Kgb(j,nr))/Kw_Kgb)*(2*rand(NP,1)-ones(NP,1)); % % % % % % % % % % % % % Motion Process % % % % % % % % % % % DX = Dt*(N(:,i)+F(:,i)); % % % % % % % % % % % % % Crossover % % % % % % % % % % % % % if C_flag ==1 C_rate = 0.8 + 0.2*(K(i)-Kgb(j,nr))/Kw_Kgb; Cr = rand(NP,1) < C_rate ; % Random selection of Krill No. for Crossover NK4Cr = round(NK*rand+.5); % Crossover scheme X(:,i)=X(:,NK4Cr).*(1-Cr)+X(:,i).*Cr; end % Update the position X(:,i)=X(:,i)+DX; X(:,i)=findlimits(X(:,i)',LB,UB,Xgb(:,j,nr)'); % Bounds Checking K(i)=cost(X(:,i)); if K(i)<Kib(i) Kib(i)=K(i); Xib(:,i)=X(:,i); end end % Update the current best [Kgb(j+1,nr), A] = min(K); if Kgb(j+1,nr)<Kgb(j,nr) Xgb(:,j+1,nr) = X(:,A); else Kgb(j+1,nr) = Kgb(j,nr); Xgb(:,j+1,nr) = Xgb(:,j,nr); end end end %% Post-Processing [Best, Ron_No] = min(Kgb(end,:)) Xgb(:,end,Ron_No) Mean = mean(Kgb(end,:)) Worst = max(Kgb(end,:)) Standard_Deviation = std(Kgb(end,:)) % Convergence plot of the best run semilogy(1:MI+1,Kgb(:,Ron_No),1:MI+1,mean(Kgb')) xlabel('{\itNo. of Iterations}') ylabel('{\itf}({\bfx_{best}})') legend('Best run values','Average run values')