无人机协同配送使用matlab代码程序

好的，以下是一个基于Matlab的无人机协同配送遗传算法代码程序的示例，供您参考：

% 设置无人机数量和任务数量
num_drones = 5;
num_tasks = 10;

% 随机生成每个任务的位置
task_locations = rand(num_tasks, 2);

% 设置遗传算法参数
options = gaoptimset('PopulationSize', 50, 'Generations', 100, 'CrossoverFcn', @crossover, 'MutationFcn', @mutation, 'SelectionFcn', @selection, 'PlotFcns', @gaplotbestf);

% 定义适应度函数
fitness_function = @(x) fitness(x, task_locations);

% 运行遗传算法
[x, fval] = ga(fitness_function, num_drones * num_tasks, [], [], [], [], zeros(num_drones * num_tasks, 1), ones(num_drones * num_tasks, 1), [], options);

% 将x矩阵转换为无人机任务分配矩阵
task_assignments = reshape(x, num_tasks, num_drones)';

% 显示结果
disp('Task assignments:');
disp(task_assignments);

% 定义适应度函数
function f = fitness(x, task_locations)
  % 将x矩阵转换为无人机任务分配矩阵
  task_assignments = reshape(x, size(task_locations, 1), [])';
  
  % 计算每个无人机的路径长度
  path_lengths = zeros(size(task_assignments, 1), 1);
  for i = 1:size(task_assignments, 1)
    path_lengths(i) = calculate_path_length(task_locations, task_assignments(i, :));
  end
  
  % 计算适应度
  f = sum(path_lengths);
end

% 计算无人机路径长度
function path_length = calculate_path_length(task_locations, task_assignment)
  % 获取所有任务的位置
  task_locations = task_locations(task_assignment == 1, :);
  
  % 计算路径长度
  path_length = 0;
  for i = 2:size(task_locations, 1)
    path_length = path_length + norm(task_locations(i, :) - task_locations(i - 1, :));
  end
end

% 定义交叉函数
function [c1, c2] = crossover(p1, p2)
  % 随机选择交叉点
  crossover_point = randi(numel(p1));
  
  % 交叉
  c1 = [p1(1:crossover_point) p2(crossover_point+1:end)];
  c2 = [p2(1:crossover_point) p1(crossover_point+1:end)];
end

% 定义变异函数
function c = mutation(p)
  % 随机选择变异点
  mutation_point = randi(numel(p));
  
  % 变异
  c = p;
  c(mutation_point) = 1 - c(mutation_point);
end

% 定义选择函数
function s = selection(population, scores)
  % 选择排名最好的两个
  [~, idx] = sort(scores);
  s = population(idx(1:2), :);
end

该代码程序使用遗传算法来解决无人机协同配送中的任务分配问题，并通过计算每个无人机的路径长度来评估解的质量。您可以根据自己的需求对代码进行修改和扩展，以更好地适应您的应用场景。