免疫算法交叉变异改进matlab代码
时间: 2023-08-25 09:06:53 浏览: 65
下面是一个使用免疫算法进行交叉和变异的示例代码,使用的是Matlab语言:
```matlab
% 定义一个抗体类
classdef Antibody
properties
antigen
affinity
end
methods
function obj = Antibody(antigen)
obj.antigen = antigen;
obj.affinity = 0;
end
end
end
% 定义一个免疫算法类
classdef ImmuneAlgorithm
properties
population_size
antigen_length
mutation_rate
crossover_rate
population
end
methods
function obj = ImmuneAlgorithm(population_size, antigen_length, mutation_rate, crossover_rate)
obj.population_size = population_size;
obj.antigen_length = antigen_length;
obj.mutation_rate = mutation_rate;
obj.crossover_rate = crossover_rate;
obj.population = Antibody.empty(population_size, 0);
for i=1:population_size
obj.population(i) = Antibody(randi([0,1], 1, antigen_length));
end
end
% 评估抗体亲和度
function evaluate_affinity(obj)
for i=1:obj.population_size
antigen = obj.population(i).antigen;
% 计算抗体亲和度
obj.population(i).affinity = sum(antigen);
end
end
% 选择抗体进行交叉和变异
function selected_population = select_antibodies(obj)
% 根据抗体亲和度排序
[~, order] = sort([obj.population.affinity], 'descend');
sorted_population = obj.population(order);
% 选择前50%的抗体进行交叉和变异
selected_population = sorted_population(1:floor(obj.population_size/2));
end
% 抗体互补性交叉
function offspring = crossover(obj, selected_population)
offspring = Antibody.empty(0, 0);
for i=1:length(selected_population)-1
for j=i+1:length(selected_population)
if rand() < obj.crossover_rate
% 交叉
antigen1 = selected_population(i).antigen;
antigen2 = selected_population(j).antigen;
offspring_antigen = antigen1 .* (rand(1, obj.antigen_length)<0.5) + antigen2 .* (rand(1, obj.antigen_length)>=0.5);
offspring(end+1) = Antibody(offspring_antigen);
end
end
end
end
% 抗体多样性变异
function offspring = mutation(obj, selected_population)
offspring = Antibody.empty(0, 0);
for i=1:length(selected_population)
if rand() < obj.mutation_rate
% 变异
antigen = selected_population(i).antigen;
mutant_antigen = xor(antigen, (rand(1, obj.antigen_length)<obj.mutation_rate));
offspring(end+1) = Antibody(mutant_antigen);
end
end
end
% 运行免疫算法
function run(obj, generations)
for i=1:generations
obj.evaluate_affinity();
selected_population = obj.select_antibodies();
offspring1 = obj.crossover(selected_population);
offspring2 = obj.mutation(selected_population);
obj.population = [selected_population, offspring1, offspring2];
end
end
end
end
```
注:此代码仅为示例,具体实现方式可能因应用场景不同而异,需要根据具体需求进行修改。