GA两个变量求函数最小值

% 主函数 clc; clear all; M = 30;N =60; %N为染色体长度、M为种群(规模)个体数 generation = 100; %遗传代数 gen = 1; %从第一代开始遗传 maxdata = -10; %初始化一个最大 pm = 0.08; %变异概率 pc = 0.60; %交叉概率 %sum1_fit_eve(0)=0.00; %总适应度 pop = round(rand(M,N)); %随机初始化矩阵，产生初始种群(取整) x = decode_x(pop(:,1:30)); %选取前半染色体 y = decode_y(pop(:,31:end));%选取后半染色体 %对x1 x2进行解码 fitness = y.*3.226+((6.452*(x.*1+y.*0.125).*(cos(x.*1)-cos(y.*2)).^2)./((0.8+(x.*1-4.2).^2).^0.5+2.*(y.*1-7).^2)); %自己定义的适应度函数 while gen < generation [B] = seclection(fitness,pop); %轮盘赌选择 [newpop] = crossover(pc,B); %单点交叉 [B] = mutation(pm,newpop); %单点变异操作 pop = B; x = decode_x(pop(:,1:30)); y = decode_y(pop(:,31:end)); fitness = y.*3.226+((6.452*(x+y.*0.125).*(cos(x)-cos(y.*2)).^2)./((0.8+(x-4.2).^2).^0.5+2.*(y-7).^2)); %计算适应度 [fit_best,index] = max(fitness);%本代中的最优目标值 sum(gen)=fit_best+sum(gen-1); sum1_fit_eve(gen)=sum(gen)/gen; %求平均适应度 if fit_best >= maxdata maxdata = fit_best; verter = pop(index,:); x_1 = decode_x(verter(1:30));%找到最优解的这个染色体 y_1 = decode_y(verter(31:end)); end num(gen) = maxdata; numx(gen) = x_1; numy(gen) = y_1; gen = gen + 1; end disp(sprintf('max=：%.15f',num(gen-1))); %输出最优解 disp(sprintf('x=：%.15f y=：%.15f',x_1,y_1));%最优解对应的x1 x2的值 figure(1) plot(num,'k');%绘制最佳适应度图形 xlabel('迭代次数'); ylabel('最佳适应度'); hold on; figure(2) %hold on; plot(numx,'r--');%绘制x最佳解图形 hold on; plot(numy,'b');%绘制y最佳解图形 legend('x值','y值') figure(3) plot(sum1_fit_eve,'b*');%绘制平均适应度图形