基于麻雀搜索算法的车辆局部路径规划matlab代码
时间: 2023-09-19 12:07:04 浏览: 63
以下是一个基于麻雀搜索算法的车辆局部路径规划的MATLAB代码示例。代码中包括了目标函数的定义、搜索群体的初始化、搜索过程和搜索结果的可视化等部分。
```matlab
% 目标函数:路径长度和平滑度
function [fitness] = objectiveFunction(path)
% 计算路径长度
length = 0;
for i = 1:length(path)-1
length = length + norm(path(i+1,:) - path(i,:));
end
% 计算路径平滑度
smoothness = 0;
for i = 2:length(path)-1
smoothness = smoothness + norm(path(i+1,:) - 2*path(i,:) + path(i-1,:));
end
% 计算适应度值
fitness = length + smoothness;
end
% 初始化搜索群体
function [birds] = initializeBirds(currentPos, numBirds, searchRange)
birds = zeros(numBirds, 2);
for i = 1:numBirds
birds(i,:) = currentPos + rand(1,2)*searchRange - searchRange/2;
end
end
% 麻雀搜索算法
function [bestPath, bestFitness] = sparrowSearch(currentPos, searchRange, numBirds, maxIterations)
% 初始化搜索群体
birds = initializeBirds(currentPos, numBirds, searchRange);
% 记录历史最优解
bestPath = currentPos;
bestFitness = objectiveFunction(currentPos);
% 进行搜索
for iter = 1:maxIterations
% 每只鸟进行搜索
for i = 1:numBirds
% 随机选择一个方向
delta = rand(1,2)*searchRange - searchRange/2;
% 计算搜索结果
newPath = birds(i,:) + delta;
newFitness = objectiveFunction(newPath);
% 如果搜索结果更优,则更新历史最优解
if newFitness < bestFitness
bestPath = newPath;
bestFitness = newFitness;
end
end
% 更新搜索群体
birds = bestPath + rand(numBirds,2)*(bestPath - currentPos);
end
end
% 测试代码:绘制搜索结果
currentPos = [0, 0];
searchRange = 10;
numBirds = 20;
maxIterations = 100;
[bestPath, bestFitness] = sparrowSearch(currentPos, searchRange, numBirds, maxIterations);
plot(bestPath(:,1), bestPath(:,2), '-o');
```
请注意,这只是一个基本的示例代码,实际应用中需要根据具体问题进行适当修改。