路径规划matlab代码

以下是两种基于Matlab的路径规划算法的代码示例：

1. 遗传算法路径规划代码示例：

% 遗传算法路径规划
function [bestPath, bestFitness] = geneticAlgorithmPathPlanning(map, startPoint, endPoint, populationSize, maxGenerations)
    % 初始化种群
    population = initializePopulation(map, startPoint, endPoint, populationSize);    
    % 进化过程
    for generation = 1:maxGenerations
        % 计算适应度
        fitness = calculateFitness(population, map);
        
        % 选择父代
        parents = selection(population, fitness);
        
        % 交叉产生子代
        offspring = crossover(parents);
        
        % 变异
        offspring = mutation(offspring);
        
        % 合并父代和子代
        population = [parents; offspring];
        
        % 选择下一代种群
        population = selection(population, fitness);
    end
    
    % 获取最佳路径和适应度
    bestFitness = min(fitness);
    bestPathIndex = find(fitness == bestFitness, 1);
    bestPath = population(bestPathIndex, :);
end

% 初始化种群
function population = initializePopulation(map, startPoint, endPoint, populationSize)
    % 初始化种群矩阵
    population = zeros(populationSize, size(map, 1));
    
    for i = 1:populationSize
        % 随机生成路径
        path = generateRandomPath(map, startPoint, endPoint);
        
        % 将路径添加到种群中
        population(i, :) = path;
    end
end

% 计算适应度
function fitness = calculateFitness(population, map)
    fitness = zeros(size(population, 1), 1);
    
    for i = 1:size(population, 1)
        % 计算路径长度作为适应度
        path = population(i, :);
        fitness(i) = calculatePathLength(path, map);
    end
end

% 选择父代
function parents = selection(population, fitness)
    % 根据适应度进行轮盘赌选择
    totalFitness = sum(fitness);
    probabilities = fitness / totalFitness;
    cumulativeProbabilities = cumsum(probabilities);
    
    parents = zeros(size(population));
    
    for i = 1:size(population, 1)
        % 选择父代
        r = rand();
        parentIndex = find(cumulativeProbabilities >= r, 1);
        parents(i, :) = population(parentIndex, :);
    end
end

% 交叉产生子代
function offspring = crossover(parents)
    offspring = zeros(size(parents));
    
    for i = 1:size(parents, 1)
        % 随机选择两个父代
        parent1 = parents(randperm(size(parents, 1), 1), :);
        parent2 = parents(randperm(size(parents, 1), 1), :);
        
        % 交叉操作
        crossoverPoint = randi([2, size(parents, 2) - 1]);
        offspring(i, :) = [parent1(1:crossoverPoint), parent2(crossoverPoint+1:end)];
    end
end

% 变异
function offspring = mutation(offspring)
    mutationRate = 0.01;
    
    for i = 1:size(offspring, 1)
        % 随机选择一个基因进行变异
        geneIndex = randi([2, size(offspring, 2) - 1]);
        
        % 变异操作
        offspring(i, geneIndex) = randi([1, size(offspring, 2)]);
    end
end

% 计算路径长度
function length = calculatePathLength(path, map)
    length = 0;
    
    for i = 1:length(path)-1
        % 计算两个节点之间的距离
        node1 = path(i);
        node2 = path(i+1);
        length = length + map(node1, node2);
    end
end

% 生成随机路径
function path = generateRandomPath(map, startPoint, endPoint)
    path = [startPoint];
    
    while path(end) ~= endPoint
        % 随机选择下一个节点
        nextNode = randi([1, size(map, 1)]);
        
        % 确保下一个节点不在路径中且与当前节点相连
        if ~ismember(nextNode, path) && map(path(end), nextNode) > 0
            path = [path, nextNode];
        end
    end
end

2. 粒子群算法路径规划代码示例：

% 粒子群算法路径规划
function [bestPath, bestFitness] = particleSwarmAlgorithmPathPlanning(map, startPoint, endPoint, populationSize, maxGenerations)
    % 初始化种群
    population = initializePopulation(map, startPoint, endPoint, populationSize);
    
    % 初始化速度
    velocity = initializeVelocity(populationSize, size(map, 1));
    
    % 初始化个体最佳位置和适应度
    individualBestPositions = population;
    individualBestFitnesses = calculateFitness(population, map);
    
    % 初始化全局最佳位置和适应度
    [globalBestFitness, globalBestIndex] = min(individualBestFitnesses);
    globalBestPosition = individualBestPositions(globalBestIndex, :);
    
    % 进化过程
    for generation = 1:maxGenerations
        % 更新速度和位置
        velocity = updateVelocity(velocity, population, individualBestPositions, globalBestPosition);
        population = updatePosition(population, velocity);
        
        % 计算适应度
        fitness = calculateFitness(population, map);
        
        % 更新个体最佳位置和适应度
        for i = 1:size(population, 1)
            if fitness(i) < individualBestFitnesses(i)
                individualBestPositions(i, :) = population(i, :);
                individualBestFitnesses(i) = fitness(i);
            end
        end
        
        % 更新全局最佳位置和适应度
        [currentBestFitness, currentBestIndex] = min(individualBestFitnesses);
        if currentBestFitness < globalBestFitness
            globalBestFitness = currentBestFitness;
            globalBestPosition = individualBestPositions(currentBestIndex, :);
        end
    end
    
    % 获取最佳路径和适应度
    bestFitness = globalBestFitness;
    bestPath = globalBestPosition;
end

% 初始化速度
function velocity = initializeVelocity(populationSize, pathLength)
    velocity = zeros(populationSize, pathLength);
end

% 更新速度
function velocity = updateVelocity(velocity, population, individualBestPositions, globalBestPosition)
    inertiaWeight = 0.9;
    cognitiveWeight = 2;
    socialWeight = 2;
    
    for i = 1:size(population, 1)
        % 更新速度
        velocity(i, :) = inertiaWeight * velocity(i, :) ...
            + cognitiveWeight * rand() * (individualBestPositions(i, :) - population(i, :)) ...
            + socialWeight * rand() * (globalBestPosition - population(i, :));
    end
end

% 更新位置
function population = updatePosition(population, velocity)
    population = population + velocity;
end