matlab基于遗传算法的路径规划的代码

由于路径规划的具体问题和要求不同，因此基于遗传算法的路径规划的代码也会有所不同。以下是一个简单的基于遗传算法的路径规划代码，供参考：

% 定义目标点
goal = [10,10];

% 定义起点
start = [0,0];

% 定义障碍物
obstacles = [3,3;4,3;5,3;6,3;7,3;7,4;7,5;7,6;6,6;5,6;4,6;3,6];

% 定义个体数和进化代数
popsize = 20;
ngen = 100;

% 定义遗传算法参数
crossover_fraction = 0.8;
mutation_rate = 0.05;

% 初始化种群
pop = zeros(popsize, length(goal)*2);
for i=1:popsize
pop(i,:) = [rand(1)*10, rand(1)*10, rand(1)*10, rand(1)*10];
end

% 进化种群
for i=1:ngen
% 计算适应度
fitness = zeros(1, popsize);
for j=1:popsize
fitness(j) = path_fitness(pop(j,:), start, goal, obstacles);
end

% 选择操作
[parent1,parent2] = select_parents(pop, fitness);

% 交叉操作
offspring = crossover(parent1, parent2, crossover_fraction);

% 变异操作
offspring = mutate(offspring, mutation_rate);

% 合并父代和子代种群
pop = [pop; offspring];

% 保留适应度最高的个体
[~, idx] = sort(fitness, 'descend');
pop = pop(idx(1:popsize), :);
end

% 输出最佳路径
best_path = pop(1,:);
path = decode_path(best_path, start, goal, obstacles);
disp(path);

% 适应度函数
function fitness = path_fitness(path, start, goal, obstacles)
path = decode_path(path, start, goal, obstacles);
fitness = 1/total_distance(path);
end

% 选择函数
function [parent1, parent2] = select_parents(pop, fitness)
[~, idx] = sort(fitness, 'descend');
parent1 = pop(idx(1),:);
parent2 = pop(idx(2),:);
end

% 交叉函数
function offspring = crossover(parent1, parent2, crossover_fraction)
crossover_point = round(crossover_fraction*length(parent1));
offspring = [parent1(1:crossover_point), parent2(crossover_point+1:end)];
end

% 变异函数
function offspring = mutate(individual, mutation_rate)
for i=1:length(individual)
if rand(1) < mutation_rate
individual(i) = rand(1)*10;
end
end
offspring = individual;
end

% 解码函数
function path = decode_path(individual, start, goal, obstacles)
path = [start];
for i=1:2:length(individual)
path(end+1,:) = [individual(i), individual(i+1)];
end
path(end+1,:) = goal;
path = smooth_path(path, obstacles);
end

% 平滑路径函数
function smooth_path = smooth_path(path, obstacles)
smooth_path = path;
while true
flag = false;
for i=2:size(smooth_path,1)-1
p1 = smooth_path(i-1,:);
p2 = smooth_path(i,:);
p3 = smooth_path(i+1,:);
if is_collision(p1, p3, obstacles)
smooth_path(i,:) = (p1+p3)/2;
flag = true;
end
end
if ~flag
break;
end
end
end

% 判断是否碰撞函数
function collision = is_collision(p1, p2, obstacles)
collision = false;
for i=1:size(obstacles,1)
o1 = obstacles(i,:);
o2 = obstacles(mod(i,size(obstacles,1))+1,:);
if is_intersect(p1,p2,o1,o2)
collision = true;
break;
end
end
end

% 判断两线段是否相交函数
function intersect = is_intersect(p1,p2,p3,p4)
intersect = false;
if ((p1(1)-p2(1))*(p3(2)-p1(2)) - (p1(2)-p2(2))*(p3(1)-p1(1))) * ((p1(1)-p2(1))*(p4(2)-p1(2)) - (p1(2)-p2(2))*(p4(1)-p1(1))) < 0 && ...
((p3(1)-p4(1))*(p1(2)-p3(2)) - (p3(2)-p4(2))*(p1(1)-p3(1))) * ((p3(1)-p4(1))*(p2(2)-p3(2)) - (p3(2)-p4(2))*(p2(1)-p3(1))) < 0
intersect = true;
end
end

% 计算距离函数
function distance = calculate_distance(p1, p2)
distance = sqrt(sum((p1-p2).^2));
end

% 计算路径总长度函数
function total_distance = total_distance(path)
total_distance = 0;
for i=2:size(path,1)
total_distance = total_distance + calculate_distance(path(i-1,:), path(i,:));
end
end