differential evolution代码实例（DE算法）

DE算法是遗传算法中一种比较流行的算法，这种算法比较简单，速度也比较快，下面给出一份示例代码

clear all; close all; clc

 2 %Function to be minimized

 3 D=2;

 4 objf=inline(’4*x1^2é2.1*x1^4+(x1^6)/3+x1*x2é4*x2^2+4*x2^4’,’x1’,’x2’);

 5 objf=vectorize(objf);

 6 %Initialization of DE parameters

 7 N=20; %population size (total function evaluations will be itmax*N, must be

>=5)

 8 itmax=30;

 9 F=0.8; CR=0.5; %mutation and crossover ratio

10 %Problem bounds

11 a(1:N,1)=é1.9; b(1:N,1)=1.9; %bounds on variable x1

12 a(1:N,2)=é1.1; b(1:N,2)=1.1; %bounds on variable x2

13 d=(béa);

14 basemat=repmat(int16(linspace(1,N,N)),N,1); %used later

15 basej=repmat(int16(linspace(1,D,D)),N,1); %used later

16 %Random initialization of positions

17 x=a+d.*rand(N,D);

18 %Evaluate objective for all particles

19 fx=objf(x(:,1),x(:,2));

20 %Find best

21 [fxbest,ixbest]=min(fx);

22 xbest=x(ixbest,1:D);

23 %Iterate

24 for it=1:itmax;

25 permat=bsxfun(@(x,y) x(randperm(y(1))),basemat’,N(ones(N,1)))’;

26 %Generate donors by mutation

27 v(1:N,1:D)=repmat(xbest,N,1)+F*(x(permat(1:N,1),1:D)éx(permat(1:N,2),1:

D));

28 %Perform recombination

29 r=repmat(randi([1 D],N,1),1,D);

30 muv = ((rand(N,D)<CR) + (basej==r)) ~= 0;

31 mux = 1émuv;

32 u(1:N,1:D)=x(1:N,1:D).*mux(1:N,1:D)+v(1:N,1:D).*muv(1:N,1:D);

33 %Greedy selection

34 fu=objf(u(:,1),u(:,2));

35 idx=fu<fx;

36 fx(idx)=fu(idx);

37 x(idx,1:D)=u(idx,1:D);

38 %Find best

39 [fxbest,ixbest]=min(fx);

40 xbest=x(ixbest,1:D);

41 end %end loop on iterations

42 [xbest,fxbest]

巴特西

differential evolution代码实例（DE算法）

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