避障遗传算法matlab
时间: 2024-01-07 11:22:39 浏览: 22
避障遗传算法是一种基于遗传算法的路径规划方法,用于解决机器人或车辆在复杂环境中避开障碍物的问题。下面是一个使用MATLAB实现避障遗传算法的示例:
```matlab
% 遗传算法参数设置
populationSize = 50; % 种群大小
maxGenerations = 100; % 最大迭代次数
mutationRate = 0.01; % 变异率
% 初始化种群
population = initializePopulation(populationSize);
% 迭代进化
for generation = 1:maxGenerations
% 计算适应度
fitness = calculateFitness(population);
% 选择父代
parents = selection(population, fitness);
% 交叉繁殖
offspring = crossover(parents);
% 变异
offspring = mutation(offspring, mutationRate);
% 更新种群
population = [parents; offspring];
end
% 选择最优个体
bestIndividual = selectBestIndividual(population, fitness);
% 输出最优路径
disp('最优路径:');
disp(bestIndividual);
% 辅助函数
function population = initializePopulation(populationSize)
% 初始化种群,每个个体表示一条路径
population = randi([0, 1], populationSize, 10);
end
function fitness = calculateFitness(population)
% 计算适应度,适应度越高表示路径越短
fitness = sum(population, 2);
end
function parents = selection(population, fitness)
% 选择父代,使用轮盘赌选择算法
totalFitness = sum(fitness);
probabilities = fitness / totalFitness;
cumulativeProbabilities = cumsum(probabilities);
parents = zeros(size(population));
for i = 1:size(population, 1)
r = rand();
index = find(cumulativeProbabilities >= r, 1);
parents(i, :) = population(index, :);
end
end
function offspring = crossover(parents)
% 交叉繁殖,使用单点交叉算法
offspring = zeros(size(parents));
for i = 1:2:size(parents, 1)
crossoverPoint = randi([1, size(parents, 2)]);
offspring(i, :) = [parents(i, 1:crossoverPoint), parents(i+1, crossoverPoint+1:end)];
offspring(i+1, :) = [parents(i+1, 1:crossoverPoint), parents(i, crossoverPoint+1:end)];
end
end
function offspring = mutation(offspring, mutationRate)
% 变异,随机翻转某些基因
for i = 1:size(offspring, 1)
for j = 1:size(offspring, 2)
if rand() < mutationRate
offspring(i, j) = 1 - offspring(i, j);
end
end
end
end
function bestIndividual = selectBestIndividual(population, fitness)
% 选择最优个体,即适应度最高的个体
[~, index] = max(fitness);
bestIndividual = population(index, :);
end
```