粒子群算法求解作业车间调度问题matlab编码

以下是一个简单的粒子群算法求解作业车间调度问题的MATLAB代码：

% 初始化问题参数
num_jobs = 5; % 作业数量
num_machines = 3; % 机器数量
processing_times = [4, 5, 3, 2, 6; % 每个作业在每个机器上的处理时间
2, 4, 6, 3, 5;
3, 5, 4, 2, 4];

% 初始化粒子群算法参数
num_particles = 20; % 粒子数量
max_iterations = 50; % 最大迭代次数
w = 0.7; % 惯性因子
c1 = 1.5; % 个体学习因子
c2 = 1.5; % 全局学习因子

% 初始化粒子群
particles = zeros(num_particles, num_jobs); % 每个粒子表示一个作业的顺序
for i = 1:num_particles
particles(i, :) = randperm(num_jobs);
end

% 计算每个粒子的适应度
fitness = zeros(num_particles, 1);
for i = 1:num_particles
fitness(i) = evaluate_fitness(particles(i, :), processing_times, num_machines);
end

% 找到最优解
[global_best_fitness, global_best_position] = min(fitness);
global_best_particle = particles(global_best_position, :);

% 迭代
for iteration = 1:max_iterations
% 更新每个粒子的速度和位置
for i = 1:num_particles
% 计算速度
velocity = w * particles(i, :) + ...
c1 * rand(1, num_jobs) .* (global_best_particle - particles(i, :)) + ...
c2 * rand(1, num_jobs) .* (best_local_particle(particles(i, :), fitness) - particles(i, :));
% 更新位置
particles(i, :) = update_position(particles(i, :), velocity);
end

% 计算每个粒子的适应度
for i = 1:num_particles
fitness(i) = evaluate_fitness(particles(i, :), processing_times, num_machines);
end

% 找到最优解
[best_fitness, best_position] = min(fitness);
best_particle = particles(best_position, :);
if best_fitness < global_best_fitness
global_best_fitness = best_fitness;
global_best_particle = best_particle;
end

% 显示当前迭代的结果
fprintf('Iteration %d: Best fitness = %f\n', iteration, global_best_fitness);
end

% 显示最终结果
fprintf('Best order = ');
disp(global_best_particle);
fprintf('Best fitness = %f\n', global_best_fitness);

% 计算适应度的函数
function fitness = evaluate_fitness(order, processing_times, num_machines)
num_jobs = length(order);
completion_times = zeros(num_jobs, num_machines);
for i = 1:num_jobs
if i == 1 % 第一个作业
completion_times(i, :) = processing_times(order(i), :);
else
completion_times(i, 1) = completion_times(i-1, 1) + processing_times(order(i), 1);
for j = 2:num_machines
completion_times(i, j) = max(completion_times(i, j-1), completion_times(i-1, j)) + processing_times(order(i), j);
end
end
end
fitness = max(completion_times(num_jobs, :));
end

% 找到局部最优解的函数
function best_particle = best_local_particle(particle, fitness)
num_particles = length(fitness);
num_jobs = length(particle);
neighborhood = zeros(3, num_jobs);
neighborhood(1, :) = [particle(2:end), particle(1)];
neighborhood(2, :) = [particle(end), particle(1:end-1)];
neighborhood(3, :) = [particle(2:end-1), particle(1), particle(end)];
best_fitness = inf;
best_particle = particle;
for i = 1:3
neighbor_fitness = evaluate_fitness(neighborhood(i, :), processing_times, num_machines);
if neighbor_fitness < best_fitness
best_fitness = neighbor_fitness;
best_particle = neighborhood(i, :);
end
end
end

% 更新位置的函数
function new_position = update_position(position, velocity)
num_jobs = length(position);
new_position = zeros(1, num_jobs);
[~, order] = sort(velocity);
for i = 1:num_jobs
new_position(i) = position(order(i));
end
end