信号灯 经济性 路口通行 matlab 代码

信号灯经济性路口通行的Matlab代码如下：

% 设置交叉口A和B的相位时间
time_A = [56, 23, 35, 26]; % A交叉口第一、二、三、四相位时间
time_B = [47, 21, 39, 22]; % B交叉口第一、二、三、四相位时间

% 定义黄灯和红灯的时间
yellow_time = 3; % 黄灯时间
red_time = 1; % 红灯时间

% 优化配时算法
% 这里可以使用不同的优化算法，如遗传算法或模拟退火算法

% 遗传算法示例
population_size = 50; % 种群大小
max_generations = 100; % 最大迭代次数

% 初始化种群
population = randi([0, 1], population_size, length(time_A));

for generation = 1:max_generations
    % 评估适应度函数，根据交通流量和延迟时间等指标来评估
    fitness = evaluate_fitness(population, time_A, time_B, yellow_time, red_time);
    
    % 选择和交叉
    new_population = selection_and_crossover(population, fitness);
    
    % 变异
    new_population = mutation(new_population);
    
    % 更新种群
    population = new_population;
end

% 选择最优解
best_solution = population(1, :);

% 输出最优解的配时方案
disp('最优的配时方案:');
for i = 1:length(best_solution)
    if best_solution(i) == 1
        disp(['相位', num2str(i), '：绿灯']);
    else
        disp(['相位', num2str(i), '：红灯']);
    end
end

% 评估适应度函数
function fitness = evaluate_fitness(population, time_A, time_B, yellow_time, red_time)
    fitness = zeros(size(population, 1), 1);
    for i = 1:size(population, 1)
        green_time_A = sum(time_A .* population(i, :));
        green_time_B = sum(time_B .* population(i, :));
        delay_A = calculate_delay(green_time_A, yellow_time, red_time);
        delay_B = calculate_delay(green_time_B, yellow_time, red_time);
        fitness(i) = calculate_fitness(delay_A, delay_B); % 根据具体的适应度计算公式计算适应度
    end
end

% 计算延迟时间
function delay = calculate_delay(green_time, yellow_time, red_time)
    delay = green_time + yellow_time + red_time;
end

% 计算适应度
function fitness = calculate_fitness(delay_A, delay_B)
    % 这里可以根据具体情况设计适应度计算公式
    fitness = delay_A + delay_B; % 示例：适应度等于A和B交叉口延迟时间之和
end

% 选择和交叉
function new_population = selection_and_crossover(population, fitness)
    % 这里可以使用不同的选择和交叉算法，如轮盘赌选择和单点交叉
    
    % 轮盘赌选择示例
    cumulative_fitness = cumsum(fitness) / sum(fitness);
    new_population = zeros(size(population));
    for i = 1:size(population, 1)
        rand_num = rand;
        idx = find(cumulative_fitness >= rand_num, 1);
        new_population(i, :) = population(idx, :);
    end
    
    % 单点交叉示例
    crossover_point = randi([1, size(population, 2)], size(population, 1), 1);
    for i = 1:size(population, 1)/2
        parent1 = new_population(i, :);
        parent2 = new_population(i+1, :);
        new_population(i, :) = [parent1(1:crossover_point(i)), parent2(crossover_point(i)+1:end)];
        new_population(i+1, :) = [parent2(1:crossover_point(i)), parent1(crossover_point(i)+1:end)];
    end
end

% 变异
function new_population = mutation(population)
    % 这里可以使用不同的变异算法，如位变异或逆序变异
    
    % 位变异示例
    mutation_rate = 0.01; % 变异概率
    new_population = population;
    for i = 1:size(new_population, 1)
        for j = 1:size(new_population, 2)
            if rand < mutation_rate
                new_population(i, j) = 1 - new_population(i, j);
            end
        end
    end
end