遗传算法多式联运路径优化matlab全部代码

遗传算法多式联运路径优化是指利用遗传算法来优化多式联运路径的路径选择问题。下面是一个用MATLAB编写的完整代码示例：

function [bestSolution, bestFitness] = geneticAlgorithm()

% 遗传算法参数设置
populationSize = 50; % 种群大小
chromosomeLength = 10; % 染色体长度
crossoverRate = 0.8; % 交叉概率
mutationRate = 0.01; % 变异概率
tournamentSize = 5; % 锦标赛规模
maxGenerations = 100; % 最大迭代次数

% 初始化种群
population = zeros(populationSize, chromosomeLength);
for i = 1:populationSize
population(i,:) = randperm(chromosomeLength);
end

% 迭代优化
for generation = 1:maxGenerations
% 计算适应度
fitness = evaluateFitness(population);

% 选择（锦标赛选择）
tournamentPopulation = zeros(populationSize, chromosomeLength);
for i = 1:populationSize
tournamentIndices = randperm(populationSize, tournamentSize);
[~, bestIndex] = max(fitness(tournamentIndices));
tournamentPopulation(i,:) = population(tournamentIndices(bestIndex),:);
end

% 交叉（单点交叉）
offspringPopulation = zeros(size(population));
for i = 1:2:populationSize
parentIndices = randperm(populationSize, 2);
if rand() < crossoverRate
crossoverPoint = randi(chromosomeLength-1);
offspringPopulation(i,:) = [tournamentPopulation(parentIndices(1),1:crossoverPoint), ...
tournamentPopulation(parentIndices(2),crossoverPoint+1:end)];
offspringPopulation(i+1,:) = [tournamentPopulation(parentIndices(2),1:crossoverPoint), ...
tournamentPopulation(parentIndices(1),crossoverPoint+1:end)];
else
offspringPopulation(i,:) = tournamentPopulation(parentIndices(1),:);
offspringPopulation(i+1,:) = tournamentPopulation(parentIndices(2),:);
end
end

% 变异（位反转变异）
for i = 1:populationSize
if rand() < mutationRate
mutatePoint = randi(chromosomeLength);
offspringPopulation(i,mutatePoint) = randperm(chromosomeLength, 1);
end
end

% 更新种群
population = offspringPopulation;
end

% 计算最佳解
fitness = evaluateFitness(population);
[bestFitness, bestIndex] = max(fitness);
bestSolution = population(bestIndex,:);

end

function fitness = evaluateFitness(population)

fitness = zeros(size(population,1), 1);
for i = 1:size(population,1)
% 计算路径的适应度，根据业务需求自行定义
fitness(i) = someFunction(population(i,:));
end

end

function value = someFunction(chromosome)

% 自定义的路径适应度计算方法，根据实际情况进行编写

end

在以上代码中，遗传算法的核心思路是首先初始化种群，并通过迭代不断优化种群，直到达到最大迭代次数。每一代中，首先根据适应度函数评估每个个体的适应度，然后通过锦标赛选择法选择优秀的个体，再使用单点交叉产生新的子代个体，并以一定概率进行位反转变异。最后，根据最大适应度值选择最佳解。适应度函数和路径适应度的具体计算方法应根据实际问题进行编写。