蚁群算法MEC计算卸载的matlab仿真代码
时间: 2023-11-07 20:53:38 浏览: 88
这是一个简单的蚁群算法MEC计算卸载的matlab仿真代码,仅供参考。
```
%蚁群算法MEC计算卸载的matlab仿真代码
%问题:在MEC网络中,如何利用蚁群算法计算卸载任务的最佳策略?
%初始化参数
numAnts = 20; %蚂蚁数量
numIterations = 100; %迭代次数
alpha = 1; %信息素重要程度
beta = 3; %启发式信息重要程度
rho = 0.1; %信息素挥发速度
Q = 1; %信息素增加强度系数
%初始化MEC网络
numMECs = 10; %MEC数量
numTasks = 5; %任务数量
numUsers = 50; %用户数量
mecCapacity = randi([10,100],1,numMECs); %MEC容量
taskSize = randi([1,10],1,numTasks); %任务大小
userDemand = randi([1,5],numUsers,numTasks); %用户需求
%初始化信息素矩阵
pheromone = ones(numTasks,numMECs) / (numTasks*numMECs);
%开始迭代
for i = 1:numIterations
%每只蚂蚁选择路径
for j = 1:numAnts
%初始化路径
path = zeros(1,numTasks);
%选择起始MEC
startMEC = randi([1,numMECs],1);
%计算起始MEC可用容量
availableCapacity = mecCapacity(startMEC);
%选择第一个任务
task = selectTask(path,pheromone,startMEC,availableCapacity,alpha,beta,numMECs,numTasks,taskSize,userDemand);
%循环选择下一个任务
while task > 0
%更新路径
path(task) = startMEC;
%更新MEC可用容量
availableCapacity = availableCapacity - taskSize(task);
%选择下一个MEC
startMEC = selectMEC(path,pheromone,availableCapacity,alpha,beta,numMECs,numTasks,taskSize,userDemand);
%选择下一个任务
task = selectTask(path,pheromone,startMEC,availableCapacity,alpha,beta,numMECs,numTasks,taskSize,userDemand);
end
%更新信息素矩阵
pheromone = updatePheromone(pheromone,path,Q);
end
end
%选择任务
function task = selectTask(path,pheromone,startMEC,availableCapacity,alpha,beta,numMECs,numTasks,taskSize,userDemand)
%计算可选任务的概率
probability = zeros(1,numTasks);
for i = 1:numTasks
%判断任务是否已经完成
if path(i) == 0
%计算任务的启发式信息
heuristic = userDemand(:,i)' / (taskSize(i) * availableCapacity);
%计算任务的概率
probability(i) = pheromone(i,startMEC)^alpha * heuristic^beta;
end
end
%选择任务
if sum(probability) > 0
probability = probability / sum(probability);
task = rouletteWheelSelection(probability);
else
task = -1;
end
end
%选择MEC
function mec = selectMEC(path,pheromone,availableCapacity,alpha,beta,numMECs,numTasks,taskSize,userDemand)
%计算可选MEC的概率
probability = zeros(1,numMECs);
for i = 1:numMECs
%判断MEC是否可用
if availableCapacity >= taskSize(path~=0 & path~=i)
%计算MEC的启发式信息
heuristic = sum(userDemand(:,path==i),2)' / availableCapacity;
%计算MEC的概率
probability(i) = pheromone(path(path~=0),i)'*alpha + heuristic^beta;
end
end
%选择MEC
if sum(probability) > 0
probability = probability / sum(probability);
mec = rouletteWheelSelection(probability);
else
mec = -1;
end
end
%更新信息素矩阵
function pheromone = updatePheromone(pheromone,path,Q)
for i = 1:size(path,2)
if path(i) > 0
pheromone(i,path(i)) = pheromone(i,path(i)) + Q;
end
end
end
%轮盘赌选择
function index = rouletteWheelSelection(probability)
r = rand();
c = cumsum(probability);
index = find(r <= c, 1, 'first');
end
```
注意:这只是一个简单的示例代码,实际应用中需要根据具体问题进行修改和扩展。