含仿真操作录像，基于Big Bang Big Crunch(BBBC)优化算法的目标优化matlab仿真

% clc; clear; close all; warning off; addpath(genpath(pwd)); rng('default'); Npar = 3; VarLow=[-5.12 -5.12 -5.12]; VarHigh = [5.12 5.12 5.12]; %BBBC N=100; MaxIter=100; XBest = rand(1,Npar).* (VarHigh - VarLow) + VarLow; FBest=fitnessFunc(XBest); GB=FBest; t = cputime; X = zeros(N, Npar); F = zeros(N, 1); for ii = 1:N X(ii,:) = rand(1,Npar).* (VarHigh - VarLow) + VarLow; F(ii) = fitnessFunc(X(ii,:)); end %Main Loop for it=1:MaxIter rng(it); num=zeros(1,Npar); for ii=1:N for jj=1:Npar num(jj)=num(jj)+(X(ii,jj)/F(ii)); end end %denominator term den=sum(1./F); %centre of mass Xc=num/den; for ii=1:N for jj=1:Npar New=X(ii,:); New(jj)=Xc(jj)+((VarHigh(jj)*rand)/it^2); end New=limiter(New,VarHigh,VarLow); %new fitness newFit=fitnessFunc(New); if newFit<F(ii) X(ii,:)=New; F(ii)=newFit; if F(ii)<FBest XBest=X(ii,:); FBest=F(ii); end end end GB=[GB FBest]; end t1=cputime; fprintf('The time taken is %3.2f seconds \n',t1-t); fprintf('The best value is :'); XBest FBest % Convergence Plot figure semilogy(0:MaxIter,GB); xlabel('Iterations'); ylabel('Objective Function (Cost)'); grid('on')