基于GA优化算法的波浪函数最优解计算,并动态显示优化值搜索过程以及局部最优解显示【包含程序操作录像,代码中文注释】

clc; clear; close all; warning off; rng('default'); global CROSS_RATE global MUTATION_RATE global POP_SIZE global DNA_SIZE global X_BOUND DNA_SIZE = 10 ; % DNA length POP_SIZE = 100 ; % population size CROSS_RATE = 0.8; % 交配概率(DNA交叉互换) MUTATION_RATE = 0.003; N_GENERATIONS = 50; X_BOUND = [0, 5]; %生成种群及环境（函数） pop = randsrc(POP_SIZE,DNA_SIZE,[0,1]); x = 0:0.025:5; y = sin(10.*x).*x + cos(2.*x).*x ; for gen = 1:N_GENERATIONS %翻译DNA，求个体表现型 individuals = translateDNA(pop); F_values = Fun(individuals) ; %数据可视化 figure(1) plt = plot(x,y,'b','linewidth',1.4);grid on axis([0 5 -10 8]),grid on hold on sct = scatter(individuals,F_values,'or'); axis([0 5 -10 8]),grid on hold off %求适应度 fitness = get_fitness(F_values); %求最高适应度及对应序号，以及最佳个体 [best_fitness,idx_of_best] = max(fitness); best_individual = translateDNA(pop(idx_of_best,:)); %交配繁殖后代 pop = select(pop, fitness); pop_copy = pop; for i = 1:POP_SIZE parent = pop(i,:); %原种群作为亲代 child = crossover(parent, pop_copy); %亲代DNA交叉互换得到子代DNA child = mutate(child); %子代基因突变 pop(i,:) = child; % 亲代被子代所取代 end end %运行结果： best_fitness; idx_of_best; best_individual;