量子粒子群算法的matlab实现，有程序说明

popsize=20; %种群规模 MAXITER=2000; %最大迭代次数 dimension=30; %维数 irange_l=-5.12; irange_r=5.12; xmax=10; %x的变化范围 sum1=0; sum2=0; mean=0; st=0; runno=10; data1=zeros(runno,MAXITER); %10*2000型矩阵 for run=1:runno T=cputime; %程序开始时间 x=(irange_r- irange_l)*rand(popsize,dimension,1) + irange_l; %初始化种群，将x的范围映射到[-5.12,5.12]空间内 pbest=x; %个体极值 gbest=zeros(1,dimension); %全局极值 for i=1:popsize f_x(i)=f3(x(i,:)); %更新个体极值，f3是什么函数 f_pbest(i)=f_x(i); end g=min(find(f_pbest==min(f_pbest(1:popsize)))); %更新全局极值 gbest=pbest(g,:); f_gbest=f_pbest(g); MINIUM=f_pbest(g); for t=1:MAXITER beta=(1-0.5)*(MAXITER-t)/MAXITER+0.5; %学习系数 mbest=sum(pbest)/popsize; for i=1:popsize fi=rand(1,dimension); p=fi.*pbest(i,:)+(1-fi).*gbest; u=rand(1,dimension); b=beta*(mbest-x(i,:)); v=-log(u); y=p+((-1).^ceil(0.5+rand(1,dimension))).*b.*v; x(i,:)=y; x(i,:)=sign(y).*min(abs(y),xmax); f_x(i)=f3(x(i,:)); if f_x(i)<f_pbest(i) pbest(i,:)=x(i,:); f_pbest(i)=f_x(i); end if f_pbest(i)<f_gbest gbest=pbest(i,:); f_gbest=f_pbest(i); end MINIUM=f_gbest; end data1(run,t)=MINIUM; if MINIUM>1e-007 mean=t; end end sum1=sum1+mean; sum2=sum2+MINIUM; %MINIUM time=cputime-T; st=st+time; end av1=sum1/10; %输出平均收验代数 av2=sum2/10; %输出平均最优解 st/10 %就是最后anw输出的解