基于遗传算法的单目标优化matlab程序代码

L = 32; N = 60; T = 100; Pc = 0.6; Pm = 0.02; for i = 1 : 1 : N x0(1, i) = unidrnd(2 ^ L - 1); x0(2, i) = unidrnd(2 ^ L - 1); end for t = 1 : 1 : T for i = 1 : 1 : N grayCode(1, i, :) = num2gray(x0(1, i), L); grayCode(2, i, :) = num2gray(x0(2, i), L); end for i = 1 : 1 : N / 2 if rand() < Pc p = unidrnd(L); for j = 1 : 1 : p - 1 grayCode(1, i + N, j) = grayCode(1, i, j); grayCode(2, i + N, j) = grayCode(2, i, j); grayCode(1, N - i + 1 + N, j) = grayCode(1, N - i + 1, j); grayCode(2, N - i + 1 + N, j) = grayCode(2, N - i + 1, j); end for j = p : 1 : L grayCode(1, i + N, j) = grayCode(1, N - i + 1, j); grayCode(2, i + N, j) = grayCode(2, N - i + 1, j); grayCode(1, N - i + 1 + N, j) = grayCode(1, i, j); grayCode(2, N - i + 1 + N, j) = grayCode(2, i, j); end end end for i = N + 1 : 1 : 2 * N for j = 1 : 1 : L for k = 1 : 1 : 2 if rand() < Pm grayCode(k, i, j) = 1 - grayCode(k, i, j); end end end end for i = N + 1 : 1 : 2 * N x0(1, i) = gray2num(grayCode(1, i, :)); x0(2, i) = gray2num(grayCode(2, i, :)); end for i = 1 : 1 : 2 * N x1(1, i) = double(x0(1, i)) / (2 ^ L - 1) * 10 - 5; x1(2, i) = double(x0(2, i)) / (2 ^ L - 1) * 10 - 5; temp(1, i) = x1(1, i) ^ 2 + x1(2, i) ^ 2; y(1, i) = 0.5 - (sin(sqrt(temp(1, i))) ^ 2 - 0.5) / (1 + 0.001 * temp(1, i)); end for i = 1 : 1 : N [a, b] = max(y); x2(:, i) = x0(:, b); y(1, b) = -inf; end for i = 1 : 1 : N x0(:, i) = x2(:, i); end end x1