MATLAB禁忌搜索求函数极值

clear all; close all; clc; xu = 5 xl = -5; L = randint(1,1,[5 11]); Ca = 5; G = 200; w = 1; tabu = []; x0 = rand(1,2)*(xu-xl)+xl; bestsofar.key = xO; xnow(1).key = xO; %%%%%%%%%%%%%%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 < G x_near = []; w = w*0.998; for i = l:Ca %%%%%%%%%%%%%%产生邻域解%%%%%%%%%%%%%% x_temp = xnow(g).key; xl = x_temp(1); x2 = x_temp(2); x_near(i,l) = xl+(2*rand-l)*w*(xu-xl); %%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%% if x_near(i,1) < xl x_near(i,1> = xl; end if x_near(i,1) > xu x_near(i,1) = xu; end x_near(i,2) = x2-f(2*rand-l)*w*(xu-xl); %%%%%%%%%%%%%%边界吸收%%%%%%%%%%%%%% if x_near(i,2) < xl x_near(i,2) = xl; end if x_near(i,2) > xu x_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%%%%%%%%%%%%%% deltal = 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 deltal <= 0 xnow(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) > L tabu(1,:) = []; end g = g+i; else if delta2 > 0 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) > L tabu(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1 else %%%%%%%%%%%%%%%判断改进解是否在禁忌表里%%%%%%%%%%%%%%% [M,N] = size(tabu); for m = 1:M if candidate(g).key(1)==tabu(m,1)... & candidate(g).key(2) == tabu(m,1) r = 1; end end if 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) > L tabu(1, : 〉 [ ]; end g = g+1; else %%%%%%%%%%%%%%%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) end end end trace(g) = func2(bestsofar.key); end bestsofar; figure plot(trace); xlabel('迭代次数') ylabel('目标函数值'） title('搜索过程最优值曲线') function y = func2(x) y = (cos(x(l)^2+x(2)^2)-0.1)/(1+0.3*(x(l)^2+x(2)^2)^2)+3;