基于栅格地图蚁群算法路径规划中精英蚂蚁matlab代码
时间: 2023-09-19 09:02:35 浏览: 307
蚁群算法是一种模拟蚁群觅食行为的计算算法,可以应用于路径规划等问题。精英蚂蚁算法是蚁群算法的一种改进,通过引入精英蚂蚁,能够进一步提高算法的收敛速度和搜索效果。
以下是基于栅格地图的蚁群算法路径规划的精英蚂蚁MATLAB代码:
```matlab
function [bestPath, shortestDistance] = antColonyPathPlanning(gridMap, nAnts, nIterations, alpha, beta, rho, q0)
% 输入参数:
% gridMap:栅格地图
% nAnts:蚂蚁数量
% nIterations:迭代次数
% alpha:信息素重要程度因子
% beta:启发函数重要程度因子
% rho:信息素蒸发因子
% q0:采取最大信息素路径的概率
% 输出结果:
% bestPath:最优路径
% shortestDistance:最短路径长度
pheromone = ones(size(gridMap)); % 信息素矩阵初始化
distance = createDistanceMatrix(gridMap); % 距离矩阵生成
for iteration = 1:nIterations
% 每只蚂蚁的初始位置
colony = repmat(struct('path', [], 'distance', []), 1, nAnts);
for ant = 1:nAnts
colony(ant).path(1) = randi([1, size(gridMap, 1)]); % 随机选择起始位置
end
% 逐步移动蚂蚁
for step = 2:size(gridMap, 1)
for ant = 1:nAnts
% 计算下一步可选择的位置的概率
available = find(~ismember(1:size(gridMap, 1), colony(ant).path));
probabilities = computeProbabilities(available, colony(ant).path(step-1), pheromone, distance, alpha, beta);
% 选择下一步位置
if rand < q0
[~, nextStepIndex] = max(probabilities);
else
nextStep = rouletteWheelSelection(probabilities);
nextStepIndex = find(available == nextStep);
end
colony(ant).path(step) = available(nextStepIndex);
% 更新路径总长度
colony(ant).distance = colony(ant).distance + distance(colony(ant).path(step-1), colony(ant).path(step));
% 更新信息素
pheromone(colony(ant).path(step-1), colony(ant).path(step)) = (1 - rho) * pheromone(colony(ant).path(step-1), colony(ant).path(step)) + rho;
end
end
% 更新最短路径和最优路径
[shortestDistance, shortestPathIndex] = min([colony.distance]);
bestPath = colony(shortestPathIndex).path;
% 更新信息素
pheromone = (1 - rho) * pheromone;
for ant = 1:nAnts
for step = 2:size(gridMap, 1)
pheromone(colony(ant).path(step-1), colony(ant).path(step)) = pheromone(colony(ant).path(step-1), colony(ant).path(step)) + (1 / colony(ant).distance);
end
end
end
end
function probabilities = computeProbabilities(available, current, pheromone, distance, alpha, beta)
probabilities = zeros(1, length(available));
total = 0;
for i = 1:length(available)
total = total + pheromone(current, available(i))^alpha * (1 / distance(current, available(i)))^beta;
end
for i = 1:length(available)
probabilities(i) = pheromone(current, available(i))^alpha * (1 / distance(current, available(i)))^beta / total;
end
end
function nextStep = rouletteWheelSelection(probabilities)
r = rand;
total = 0;
nextStep = 0;
for i = 1:length(probabilities)
total = total + probabilities(i);
if r <= total
nextStep = i;
break;
end
end
end
```
以上代码实现了基于栅格地图的蚁群算法路径规划,包括初始化信息素、计算概率、选择下一步位置、更新信息素等步骤。运行该代码,即可得到最优路径和最短路径长度的结果。
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