% 初始化参数
num_bees = 50; % 蜜蜂数量
max_iter = 100; % 最大迭代次数
num_drones = 3; % 无人机数量
num_waypoints = 10; % 路径关键点数量
dim = num_drones * num_waypoints * 2; % 搜索空间维度
% 障碍物信息
obstacles = [5 5 2; 15 15 3]; % 障碍物坐标和半径
safe_dist = 1; % 无人机之间的安全距离
% 初始化蜜蜂种群
bees = rand(num_bees, dim);
% 迭代更新
for iter = 1:max_iter
% 引领蜂阶段
for i = 1:num_bees
% 生成新的解
new_bee = bees(i, :);
% 选择一个随机维度进行更新
k = randi(dim);
% 选择另一个随机蜜蜂
j = randi(num_bees);
while j == i
j = randi(num_bees);
end
% 更新新解
phi = rand;
new_bee(k) = new_bee(k) + phi * (bees(j, k) - new_bee(k));
% 计算适应度
fitness_new = fitness_function(new_bee, num_drones, num_waypoints, obstacles, safe_dist);
fitness_old = fitness_function(bees(i, :), num_drones, num_waypoints, obstacles, safe_dist);
% 如果新解更优，替换旧解
if fitness_new < fitness_old
bees(i, :) = new_bee;
end
end
% 跟随蜂阶段
fitnesses = zeros(num_bees, 1);
for i = 1:num_bees
fitnesses(i) = fitness_function(bees(i, :), num_drones, num_waypoints, obstacles, safe_dist);
end
probabilities = fitnesses / sum(fitnesses);
for i = 1:num_bees
% 根据概率选择引领蜂
r = rand;
selected_bee = 1;
cumulative_prob = probabilities(1);
while cumulative_prob < r
selected_bee = selected_bee + 1;
cumulative_prob = cumulative_prob + probabilities(selected_bee);
end
% 生成新的解
new_bee = bees(selected_bee, :);
% 选择一个随机维度进行更新
k = randi(dim);
% 选择另一个随机蜜蜂
j = randi(num_bees);
while j == selected_bee
j = randi(num_bees);
end
% 更新新解
phi = rand;
new_bee(k) = new_bee(k) + phi * (bees(j, k) - new_bee(k));
% 计算适应度
fitness_new = fitness_function(new_bee, num_drones, num_waypoints, obstacles, safe_dist);
fitness_old = fitness_function(bees(selected_bee, :), num_drones, num_waypoints, obstacles, safe_dist);
% 如果新解更优，替换旧解
if fitness_new < fitness_old
bees(selected_bee, :) = new_bee;
end
end
% 侦察蜂阶段
% 检查是否有陷入局部最优的蜜蜂
% 如果有，重新初始化该蜜蜂
% 这里简单设置一个阈值，连续多次未更新则重新初始化
stagnation_threshold = 10;
stagnation_count = zeros(num_bees, 1);
if iter > 1
for i = 1:num_bees
if fitnesses(i) == prev_fitnesses(i)
stagnation_count(i) = stagnation_count(i) + 1;
else
stagnation_count(i) = 0;
end
if stagnation_count(i) >= stagnation_threshold
bees(i, :) = rand(1, dim);
end
end
end
prev_fitnesses = fitnesses;
% 输出当前最优解
[best_fitness, best_index] = min(fitnesses);
best_solution = bees(best_index, :);
fprintf('Iteration %d: Best fitness = %f\n', iter, best_fitness);
end
% 输出最终结果
[best_fitness, best_index] = min(fitnesses);
best_solution = bees(best_index, :);
disp('Final best solution:');
disp(best_solution);
% 定义适应度函数
function fitness = fitness_function(solution, num_drones, num_waypoints, obstacles, safe_dist)
% 将 solution 向量转换为每个无人机的路径
paths = reshape(solution, num_waypoints, 2, num_drones);
% 初始化路径长度、碰撞惩罚和高度变化
path_lengths = zeros(num_drones, 1);
collision_penalty = 0;
% 计算每个无人机的路径长度
for i = 1:num_drones
path = squeeze(paths(:, :, i));
path_lengths(i) = sum(sqrt(sum(diff(path).^2, 2)));
% 计算与障碍物的碰撞惩罚
for k = 1:size(obstacles, 1)
d = pdist2(path, obstacles(k, 1:2));
collision = any(d < obstacles(k, 3));
collision_penalty = collision_penalty + 1000 * collision; % 单次碰撞即重罚
end
end
% 计算多无人机之间的碰撞惩罚
for i = 1:num_drones - 1
for j = i + 1:num_drones
min_dist = min(pdist2(paths(:, :, i), paths(:, :, j)));
if any(min_dist < safe_dist)
collision_penalty = collision_penalty + 1000; % 无人机间碰撞重罚
end
end
end
% 计算总路径长度
total_path_length = sum(path_lengths);
% 加权得分
fitness = 0.6 * total_path_length + 0.3 * collision_penalty;
end
能否使该matlab数据以三维图像的形式展示
建议重罚新能源汽车自燃车企
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