请用matlab代码复现本篇文献以及其创新点

复现这篇文献中的改进多目标粒子群算法（IMOPSO）及其在物流节点选址模型中的应用涉及多个步骤。以下是MATLAB代码的主要部分，涵盖了算法的核心逻辑和创新点：

### 1. 初始化参数

% 参数设置
num_particles = 50; % 粒子数量
max_iter = 300;     % 最大迭代次数
archive_size = 100;  % 外部存档大小
c1 = 2;             % 加速度因子
c2 = 2;             % 加速度因子
w_max = 0.9;        % 最大惯性权重
w_min = 0.4;        % 最小惯性权重

% 初始化粒子群
particles = initializeParticles(num_particles);
velocities = zeros(size(particles));
personal_best = particles;
global_best = [];

% 初始化外部存档
external_archive = [];

### 2. 计算适应度值

function [fitness, cost, satisfaction] = computeFitness(particle)
    % 计算物流系统运营成本和顾客时间满意度
    cost = calculateCost(particle);
    satisfaction = calculateSatisfaction(particle);
    fitness = [cost, satisfaction];
end

function cost = calculateCost(particle)
    % 计算物流系统运营成本
    % 这里需要根据具体的数据和模型进行实现
    % 示例：cost = sum(particle .* fixed_costs + particle .* transportation_costs + storage_costs);
end

function satisfaction = calculateSatisfaction(particle)
    % 计算顾客时间满意度
    % 这里需要根据具体的数据和模型进行实现
    % 示例：satisfaction = sum(time_satisfaction_function(service_times));
end

### 3. 更新粒子速度和位置

for iter = 1:max_iter
    w = w_max - (w_max - w_min) * iter / max_iter; % 更新惯性权重
    
    for i = 1:num_particles
        % 计算适应度值
        [fitness(i,:), cost(i), satisfaction(i)] = computeFitness(particles(i,:));
        
        % 更新个人最佳位置
        if dominates(fitness(i,:), personal_best_fitness(i,:))
            personal_best(i,:) = particles(i,:);
            personal_best_fitness(i,:) = fitness(i,:);
        end
        
        % 更新全局最佳位置
        if ~isempty(global_best)
            if dominates(fitness(i,:), global_best_fitness)
                global_best = particles(i,:);
                global_best_fitness = fitness(i,:);
            end
        else
            global_best = particles(i,:);
            global_best_fitness = fitness(i,:);
        end
    end
    
    % 更新外部存档
    external_archive = updateArchive(external_archive, fitness, particles);
    
    % 更新粒子速度和位置
    for i = 1:num_particles
        velocities(i,:) = w * velocities(i,:) ...
                          + c1 * rand * (personal_best(i,:) - particles(i,:)) ...
                          + c2 * rand * (global_best - particles(i,:));
        particles(i,:) = particles(i,:) + velocities(i,:);
        
        % 变异操作
        if rand < dynamicMutationProbability(iter, max_iter)
            particles(i,:) = mutate(particles(i,:));
        end
    end
end

### 4. 更新外部存档

function archive = updateArchive(archive, new_fitness, new_particles)
    combined_fitness = [archive(:,1:2); new_fitness];
    combined_particles = [archive(:,3:end); new_particles];
    
    % 使用Pareto支配关系和拥挤距离更新存档
    nondominated_indices = findNonDominated(combined_fitness);
    nondominated_fitness = combined_fitness(nondominated_indices, :);
    nondominated_particles = combined_particles(nondominated_indices, :);
    
    if size(nondominated_fitness, 1) > archive_size
        crowding_distances = calculateCrowdingDistance(nondominated_fitness);
        [~, sorted_indices] = sort(crowding_distances, 'descend');
        archive = [nondominated_fitness(sorted_indices(1:archive_size), :) ...
                   nondominated_particles(sorted_indices(1:archive_size), :)];
    else
        archive = [nondominated_fitness nondominated_particles];
    end
end

### 5. 变异操作

function mutated_particle = mutate(particle)
    for i = 1:length(particle)
        if rand < 0.1 % 变异概率
            particle(i) = particle(i) * (1 + rand);
        end
    end
    mutated_particle = particle;
end

### 6. 动态变异概率

function pm = dynamicMutationProbability(iter, max_iter)
    pm = (1 - iter/max_iter)^3;
end

### 7. 主函数

% 主函数
[particles, velocities, personal_best, global_best, external_archive] = main();

### 8. 辅助函数

function dominates = dominates(a, b)
    % 判断a是否支配b
    dominates = all(a <= b) && any(a < b);
end

function distances = calculateCrowdingDistance(fitness)
    num_objs = size(fitness, 2);
    num_points = size(fitness, 1);
    distances = zeros(num_points, 1);
    
    for obj_idx = 1:num_objs
        [sorted_fitness, idx] = sort(fitness(:, obj_idx));
        distances(idx(1)) = Inf;
        distances(idx(end)) = Inf;
        for i = 2:num_points-1
            distances(idx(i)) = distances(idx(i)) + (sorted_fitness(i+1) - sorted_fitness(i-1));
        end
    end
end

function indices = findNonDominated(fitness)
    num_points = size(fitness, 1);
    dominated_count = zeros(num_points, 1);
    dominance_matrix = false(num_points, num_points);
    
    for i = 1:num_points
        for j = 1:num_points
            if i ~= j
                if dominates(fitness(i,:), fitness(j,:))
                    dominated_count(j) = dominated_count(j) + 1;
                    dominance_matrix(i,j) = true;
                end
            end
        end
    end
    
    indices = find(dominated_count == 0);
end

function particles = initializeParticles(num_particles)
    % 初始化粒子
    particles = rand(num_particles, num_dimensions); % 这里的num_dimensions需要根据具体问题确定
end

### 创新点
1. **外部存档的更新**：采用了Pareto支配关系和拥挤距离来更新外部存档，确保非劣解分布的均匀性。
2. **粒子学习样本的选择**：基于支配关系和粒子之间的欧拉距离选择粒子的学习样本，提高了算法的性能。
3. **动态变异操作**：引入了动态变异概率，以减缓收敛速度和增加解的多样性，避免陷入局部最优。

以上代码框架展示了如何在MATLAB中实现改进的多目标粒子群算法（IMOPSO）及其在物流节点选址模型中的应用。你需要根据具体的数据和模型细节进一步完善和调试相关函数。