背包问题基于matlab离散粒子群算法求解0-1背包问题【含Matlab源码 1342期】.zip

%%%%%%%%%%离散粒子群算法解决0-1背包问题%%%%%%%%%%% %%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%% clear all; %清除所有变量 close all; %清图 clc; %清屏 N=100; %群体粒子个数 D=10; %粒子维数 T=200; %最大迭代次数 c1=1.5; %学习因子1 c2=1.5; %学习因子2 Wmax=0.8; %惯性权重最大值 Wmin=0.4; %惯性权重最小值 Vmax=10; %速度最大值 Vmin=-10; %速度最小值 V = 300; %背包容量 C = [95,75,23,73,50,22,6,57,89,98]; %物品体积 W = [89,59,19,43,100,72,44,16,7,64]; %物品价值 afa = 2; %惩罚函数系数 %%%%%%%%%初始化种群个体（限定位置和速度）%%%%%%%%%% x=rand(N,D); %随机获得二进制编码的初始种群 v=rand(N,D) * (Vmax-Vmin)+Vmin; %%%%%%%%%%%初始化个体最优位置和最优值%%%%%%%%%%%% p=x; pbest=ones(N,1); for i=1:N pbest(i)= func4(x(i,:),C,W,V,afa); end %%%%%%%%%%%%初始化全局最优位置和最优值%%%%%%%%%%% g=ones(1,D); gbest=eps; for i=1:N if(pbest(i)>gbest) g=p(i,:); gbest=pbest(i); end end gb=ones(1,T); %%%%%%%按照公式依次迭代直到满足精度或者迭代次数%%%%%%% for i=1:T for j=1:N %%%%%%更新个体最优位置和最优值%%%%%%%%%%%%% if (func4(x(j,:),C,W,V,afa)>pbest(j)) p(j,:)=x(j,:); pbest(j)=func4(x(j,:),C,W,V,afa); end %%%%%%%%%%更新全局最优位置和最优值%%%%%%%%% if(pbest(j)>gbest) g=p(j,:); gbest=pbest(j); end %%%%%%%%%%计算动态惯性权重值%%%%%%%%%%%%% w=Wmax-(Wmax-Wmin)*i/T; %%%%%%%%%%跟新位置和速度值%%%%%%%%%%%%%% v(j,:)=w*v(j,:)+c1*rand*(p(j,:)-x(j,:))... +c2*rand*(g-x(j,:)); %%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%% for ii=1:D if (v(j,ii)>Vmax) | (v(j,ii)< Vmin) v(j,ii)=rand * (Vmax-Vmin)+Vmin; end end vx(j,:)=1./(1+exp(-v(j,:))); for jj=1:D if vx(j,jj)>rand x(j,jj)=1; else x(j,jj)=0; end end end %%%%%%%%%%%%%记录历代全局最优值%%%%%%%%%%%% gb(i)=gbest; end g %最优个体 figure plot(gb) xlabel('迭代次数'); ylabel('适应度值'); title('适应度进化曲线')