GAsuiji.zip_sparse genetic_遗传算法 _遗传算法改进_随机稀疏线阵_随机阵

%%%%%%遗传算法随机稀疏线阵%%%%%%%% %%%%%%%%%初始化参数%%%%%%%%%%%% clear all; close all; clc; NP=50;%种群数量 Pc=0.8;%交叉率 Pm=0.05;%变异率 d=0.5;%满阵阵元间距，半倍波长 lamda=1;%波长 seta0=0*pi/180;%波束指向 G=120;%最大遗传代数 d=0.5;%半倍波长 dd=50;%阵列口径，可算出满阵个数为101 L=50;%阵元个数 NN=1800;%扫描角度间隔，根据阵元数确定 Xx=0; Xs=dd-d*(L-1); %%%%%%%%%%%%%生成初始种群%%%%%%%%%%%%%% %%%%%%%%%%%%%0为没有阵元，1为有阵元%%%%%%%%%%%%% D=d*ones(L,NP);%生成均匀间隔序列 D(1,:)=zeros(1,NP); D=cumsum(D,1);%cumsum(A,1) 返回的是沿着第一维（各列）的累加和 f=rand(L,NP)*(Xs-Xx)+Xx;%把搜索空间从 [0, R] 减小至 [0, R ? (L ? 1)d r ] f(1,:)=zeros(1,NP); f=sort(f);%对每列进行升序排列处理并记录升序的位置信息 ff=f+D; ff(end,:)=ones(1,NP)*dd;%强制尾基因为孔径大小 %%%%%%%%%%%%%%遗传算法循环%%%%%%%%%%%%%% for k=1:G k %%%%%%%%%%%%%%计算适应度，即最大旁瓣%%%%%%%%%%%% for i=1:NP Fit(i)=-func_sparse_line(lamda,NN,seta0,ff(:,i)); end maxFit=max(Fit);%最大值 minFit=min(Fit);%最小值 rr=find(Fit==maxFit);%找出最大值 ffBest=ff(:,rr(1,1));%历代最优个体 Fit=(Fit-minFit)/(maxFit-minFit);%归一化适应度值 %%%%%%%%%%%%%%%基于轮盘赌的选择操作%%%%%%%%%%%%%%%% sum_Fit=sum(Fit); fitvalue=Fit./sum_Fit; fitvalue=cumsum(fitvalue); ms=sort(rand(NP,1)); fiti=1; newi=1; while newi<=NP if (ms(newi))<fitvalue(fiti) nf(:,newi)=f(:,fiti); newi=newi+1; else fiti=fiti+1; end end %%%%%%%%%%%%%%%%基于概率的交叉操作%%%%%%%%%%%%%%%%%%% for i=1:2:NP p=rand; if p<Pc q=randi([0,1],1,L); for j=1:L if q(j)==1; temp=nf(j,i+1); nf(j,i+1)=nf(j,i); nf(j,i)=temp; end end end end %%%%%%%%%%%%%%%%基于概率的变异操作%%%%%%%%%%%%%%%%%%% for m=1:NP for n=1:L r=rand; if r<Pm nf(n,m)=rand*(Xs-Xx)+Xx; end end end f=sort(nf);%对每列进行升序排列处理并记录升序的位置信息 ff=f+D; ff(1,:)=zeros(1,NP); ff(end,:)=ones(1,NP)*dd; ff(:,1)=ffBest;%保留最优个体在新种群中 ttrace(k)=maxFit;%历代最优适应度值 end figure(4) plot(ttrace,'k') xlabel('进化代数') ylabel('适应度值') title('适应度进化曲线') grid on save ffBest2.mat ffBest%存储优化结果 save ttrace2.mat ttrace%存储优化结果