Matlab遗传算法优化问题求解的演示样例代码
代码例如以下:
function m_main() clear clc Max_gen = 100;% 执行代数 pop_size = 100;%种群大小 chromsome = 10;%染色体的长度 pc = 0.9;%交叉概率 pm = 0.25;%变异概率 gen = 0;%统计代数 %初始化 init = 40*rand(pop_size, chromsome)-20; pop = init; fit = obj_fitness(pop); [max_fit, index_max] = max(fit); maxfit = max_fit; [min_fit, index_min] = min(fit); best_indiv = pop(index_max, :); %迭代操作 while gen<Max_gen gen = gen+1; bt(gen) = max_fit; if maxfit<max_fit; maxfit = max_fit; pop(index_min, :) = pop(index_max, :); best_indiv = pop(index_max, :); end best_indiv_tmp(gen) = pop(index_max); newpop = ga(pop, pc, pm, chromsome, fit); fit = obj_fitness(newpop); [max_fit, index_max] = max(fit); [min_fit, index_min] = min(fit); pop = newpop; trace(1, gen) = max_fit; trace(2, gen) = sum(fit)./length(fit); end %执行结果 [f_max gen_ct] = max(bt)%求的最大值以及代数 maxfit best_indiv %绘图 % bt hold on plot(trace(1, :), '.g:'); plot( trace(2, :), '.r-'); title('实验结果图') xlabel('迭代次数/代'), ylabel('最佳适应度(最大值)');%坐标标注 plot(gen_ct-1, 0:0.1:f_max+1, 'c-');%画出最大值 text(gen_ct, f_max+1, '最大值') hold off function [fitness] = obj_fitness(pop) %适应度计算函数 [r c] = size(pop); x = pop; fitness = zeros(r, 1); for i = 1:r for j = 1:c fitness(i, 1) = fitness(i, 1)+sin(sqrt(abs(40*x(i))))+1-abs(x(i))/20.0; end end end function newpop = ga(pop, pc, pm, chromsome, fit) pop_size = size(pop, 1); %轮盘赌选择 ps = fit/sum(fit); pscum = cumsum(ps);%size(pscum) r = rand(1, pop_size); qw = pscum*ones(1, pop_size); selected = sum(pscum*ones(1, pop_size)<ones(pop_size, 1)*r)+1; newpop = pop(selected, :); %交叉 if pop_size/2 ~= 0 pop_size = pop_size-1; end for i = 1:2:pop_size-1 while pc>rand c_pt = round(8*rand+1); pop_tp1 = newpop(i, :);pop_tp2 = newpop(i+1, :); newpop(i+1, 1:c_pt) = pop_tp1(1, 1:c_pt); newpop(i, c_pt+1:chromsome) = pop_tp2(1, c_pt+1:chromsome); end end % 变异 for i = 1:pop_size if pm>rand m_pt = 1+round(9*rand); newpop(i, m_pt) = 40*rand-20; end end end end