首页 > 代码库 > Matlab遗传算法优化问题求解的示例代码
Matlab遗传算法优化问题求解的示例代码
代码如下:
function m_main()clearclcMax_gen = 100;% 运行代数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%运行结果[f_max gen_ct] = max(bt)%求的最大值以及代数maxfitbest_indiv%画图% bthold onplot(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 endend
声明:以上内容来自用户投稿及互联网公开渠道收集整理发布,本网站不拥有所有权,未作人工编辑处理,也不承担相关法律责任,若内容有误或涉及侵权可进行投诉: 投诉/举报 工作人员会在5个工作日内联系你,一经查实,本站将立刻删除涉嫌侵权内容。