1 简介
2 完整代码
%%%%%%%%%%%%%%%%禁忌搜索算法求函数极值问题%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%clear all; %清除所有变量close all; %清图clc;%清屏xu=5; %上界xl=-5; %下界L=randi([5 11],1,1); %禁忌长度取5,11之间的随机数Ca=5; %邻域解个数Gmax=200;%禁忌算法的最大迭代次数；w=1;%自适应权重系数tabu=[];%禁忌表x0=rand(1,2)*(xu-xl)+xl;%随机产生初始解bestsofar.key=x0; %最优解xnow(1).key=x0; %当前解%%%%%%%%%%%%%%%%最优解函数值，当前解函数值%%%%%%%%%%%%%%%%%bestsofar.value=func2(bestsofar.key);xnow(1).value=func2(xnow(1).key);g=1;while g<Gmaxx_near=[]; %邻域解w=w*0.998;for i=1:Ca%%%%%%%%%%%%%%%%%%%%%产生邻域解%%%%%%%%%%%%%%%%%%%%x_temp=xnow(g).key;x1=x_temp(1);x2=x_temp(2);x_near(i,1)=x1+(2*rand-1)*w*(xu-xl);%%%%%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%边界吸收%%%%%%%%%%%%%%%%%%%%%if x_near(i,1)<xlx_near(i,1)=xl;endif x_near(i,1)>xux_near(i,1)=xu;endx_near(i,2)=x2+(2*rand-1)*w*(xu-xl);%%%%%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%边界吸收%%%%%%%%%%%%%%%%%%%%%if x_near(i,2)<xlx_near(i,2)=xl;endif x_near(i,2)>xux_near(i,2)=xu;end%%%%%%%%%%%%%%计算邻域解点的函数值%%%%%%%%%%%%%%%%%%%fitvalue_near(i)=func2(x_near(i,:));end%%%%%%%%%%%%%%%%%%%%最优邻域解为候选解%%%%%%%%%%%%%%%%%%%temp=find(fitvalue_near==max(fitvalue_near));candidate(g).key=x_near(temp,:);candidate(g).value=func2(candidate(g).key);%%%%%%%%%%%%%%候选解和当前解的评价函数差%%%%%%%%%%%%%%%%%%delta1=candidate(g).value-xnow(g).value;%%%%%%%%%%%%%%候选解和目前最优解的评价函数差%%%%%%%%%%%%%%%delta2=candidate(g).value-bestsofar.value;%%%%%候选解并没有改进解，把候选解赋给下一次迭代的当前解%%%%%%if delta1<=0xnow(g+1).key=candidate(g).key;xnow(g+1).value=func2(xnow(g).key);%%%%%%%%%%%%%%%%%%%%%更新禁忌表%%%%%%%%%%%%%%%%%%%%%%%tabu=[tabu;xnow(g+1).key];if size(tabu,1)>Ltabu(1,:)=[];endg=g+1; %更新禁忌表后，迭代次数自增1%%%%%%%如果相对于当前解有改进，则应与目前最优解比较%%%%%%%%%%elseif delta2>0 %候选解比目前最优解优%%%%%%%%%%把改进解赋给下一次迭代的当前解%%%%%%%%%%%%xnow(g+1).key=candidate(g).key;xnow(g+1).value=func2(xnow(g+1).key);%%%%%%%%%%%%%%%%%%%%更新禁忌表%%%%%%%%%%%%%%%%%%%%%tabu=[tabu;xnow(g+1).key];if size(tabu,1)>Ltabu(1,:)=[];end%%%%%%%%把改进解赋给下一次迭代的目前最优解%%%%%%%%%%%%%%%%%%%%%%%%%%%%%包含藐视准则%%%%%%%%%%%%%%%%%%%%%%%bestsofar.key=candidate(g).key;bestsofar.value=func2(bestsofar.key);g=g+1;%更新禁忌表后，迭代次数自增1else%%%%%%%%%%%%%%%判断改进解时候在禁忌表里%%%%%%%%%%%%%%%[M,N]=size(tabu);r=0;for m=1:Mif candidate(g).key(1)==tabu(m,1)...& candidate(g).key(2) == tabu(m,1)r=1;endendif r==0%%改进解不在禁忌表里，把改进解赋给下一次迭代的当前解xnow(g+1).key=candidate(g).key;xnow(g+1).value=func2(xnow(g+1).key);%%%%%%%%%%%%%%%%%%%%%更新禁忌表%%%%%%%%%%%%%%%%%%tabu=[tabu;xnow(g).key];if size(tabu,1)>Ltabu(1,:)=[];endg=g+1;%更新禁忌表后，迭代次数自增1else%%%如果改进解在禁忌表里，用当前解重新产生邻域解%%%%%xnow(g).key=xnow(g).key;xnow(g).value=func2(xnow(g).key);endendendtrace(g)=func2(bestsofar.key);endbestsofar; %最优变量及最优值figureplot(trace);xlabel('迭代次数')ylabel('目标函数值')title('搜索过程最优值曲线')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%适配值函数%%%%%%%%%%%%%%%%%%%%%%%%function y=func2(x)y=(cos(x(1)^2+x(2)^2)-0.1)/(1+0.3*(x(1)^2+x(2)^2)^2)+3;
3 运行结果
4 参考文献
[1]戚峰, 俞晶菁, 黄召杰. 基于禁忌搜索算法求解车间作业调度问题[J]. 兰州交通大学学报, , 30(3):7.
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