MATLAB智能算法 - Immunity Algorithm免疫算法（IMA解决TSP问题、IMA解决非线性问题求解）

clear clc tic pop_size = 15; chromosome_size = 10; epochs = 50; cross_rate = 0.4; mutation_rate = 0.1; a0 = 0.7; zpop_size = 5; best_fitness = 0; nf = 0; number = 0; Image = imread('bird.bmp'); q = isRgb(Image); if q == 1 Image = rgb2gray(Image); end [m, n] = size(Image); p = imhist(Image); p = p'; p = p / (m * n); figure(1); subplot(121); imshow(Image); title('原始图片'); hold on; pop = round(rand(pop_size, chromosome_size)); for epoch = 1: epochs [fitness, threshold, number] = fitnessty(pop, chromosome_size, Image, pop_size, m, n, number); if max(fitness) > best_fitness best_fitness = max(fitness); nf = 0; best_index = find(fitness == best_fitness); thres = threshold(1, best_index(1)); elseif max(fitness) == best_fitness nf = nf + 1; end if nf >= 20 fprintf('提前结束测试'); break; end similar_chromosome = similarChromosome(pop); f = fit(similar_chromosome, fitness); pop = select(pop, f); pop = cross(pop, cross_rate, pop_size, chromosome_size); pop = mutation(pop, mutation_rate, chromosome_size, pop_size); similar_population = similarPopulation(pop); if similar_population > a0 % 防止早熟 zpop = round(rand(zpop_size, chromosome_size)); pop(pop_size + 1: pop_size + zpop_size, :) = zpop(:, :); [fitness, threshold, number] = fitnessty(pop, chromosome_size, Image, pop_size, m, n, number); similar_chromosome = similarChromosome(pop); f = fit(similar_chromosome, fitness); pop = select(pop, f); end if epoch == epochs [fitness, threshold, number] = fitnessty(pop, chromosome_size, Image, pop_size, m, n, number); end drawResult(Image, thres); subplot(122) fprintf('threshold = %d', thres); % title('分割后的结果'); end toc subplot(122); drawResult(Image, thres); title('分割以后的结果');