人工鱼群算法matlab带容量限制的路径优化问题代码

以下是使用人工鱼群算法解决带容量限制的路径优化问题的MATLAB代码示例：

clc;
clear all;
close all;

%% 初始化数据
n = 50; % 鱼群大小
dim = 30; % 解的维度
max_iter = 100; % 最大迭代次数
w = 0.6; % 惯性权重
c1 = 1.5; % 个体学习因子
c2 = 1.5; % 社会学习因子
L = 5; % 邻域大小
step = 0.1; % 步长
capacity = 50; % 容量限制
demand = [10 20 30 5 15 25 20 10 5 10]; % 每个节点的需求量
location = rand(dim,length(demand)); % 节点位置
population = rand(dim,n,length(demand)); % 鱼群位置
velocity = rand(dim,n,length(demand)); % 鱼群速度
fitness = zeros(n,1); % 适应度值
best_fitness = inf; % 最佳适应度值
best_solution = zeros(dim,length(demand)); % 最佳解
capacity_left = capacity * ones(1,length(demand)); % 剩余容量

%% 迭代
for iter = 1:max_iter
    for i = 1:n
        % 更新鱼群位置和速度
        [population(:,i,:), velocity(:,i,:)] = updateFish(population(:,i,:), velocity(:,i,:), w, c1, c2, L, step, best_solution, demand, location, capacity_left);
        
        % 计算适应度值
        fitness(i) = evaluate(population(:,i,:), demand, location);
        
        % 更新最佳解
        if fitness(i) < best_fitness
            best_fitness = fitness(i);
            best_solution = population(:,i,:);
        end
    end
    
    % 输出结果
    disp(['Iteration ', num2str(iter), ': Best fitness = ', num2str(best_fitness)]);
end

%% 函数定义
function [new_population, new_velocity] = updateFish(population, velocity, w, c1, c2, L, step, best_solution, demand, location, capacity_left)
    [dim, num_nodes] = size(population);
    new_population = population;
    new_velocity = velocity;
    
    % 计算适应度值和排序
    fitness = evaluate(new_population, demand, location);
    [fitness, idx] = sort(fitness);
    
    % 更新鱼群位置和速度
    for i = 1:num_nodes
        % 个体学习
        move1 = c1 * rand(dim,1) .* (new_population(:,i) - population(:,i));
        % 社会学习
        move2 = c2 * rand(dim,1) .* (best_solution(:,i) - population(:,i));
        % 鱼群运动
        new_velocity(:,i) = w * velocity(:,i) + move1 + move2;
        % 限制速度
        new_velocity(:,i) = min(new_velocity(:,i), step);
        new_velocity(:,i) = max(new_velocity(:,i), -step);
        % 更新位置
        new_population(:,i) = new_population(:,i) + new_velocity(:,i);
        % 限制位置
        new_population(:,i) = min(new_population(:,i), 1);
        new_population(:,i) = max(new_population(:,i), 0);
        
        % 邻域搜索
        for j = 1:L
            % 随机选择邻居
            idx_neighbor = randi([1,num_nodes]);
            while idx_neighbor == i
                idx_neighbor = randi([1,num_nodes]);
            end
            
            % 判断是否更新位置
            if fitness(i) < fitness(idx_neighbor)
                % 计算位置差
                diff = new_population(:,idx_neighbor) - new_population(:,i);
                % 计算距离
                distance = norm(diff);
                if distance < step
                    % 更新位置
                    new_population(:,i) = new_population(:,i) + diff;
                    % 限制位置
                    new_population(:,i) = min(new_population(:,i), 1);
                    new_population(:,i) = max(new_population(:,i), 0);
                end
            end
        end
        
        % 判断容量限制
        for k = 1:length(demand)
            % 计算已使用容量
            used_capacity = sum(demand(1:k-1)) + sum(demand(k:i));
            if used_capacity > capacity_left(k)
                % 超出容量限制，回退到上一个节点
                new_population(:,i,k:end) = population(:,i,k:end);
                new_velocity(:,i,k:end) = -velocity(:,i,k:end);
                break;
            end
        end
        
        % 更新剩余容量
        for k = 1:length(demand)
            capacity_left(k) = max(0, capacity_left(k) - demand(k));
        end
    end
end

function fitness = evaluate(population, demand, location)
    [dim, num_nodes, num_demands] = size(population);
    fitness = zeros(num_nodes,1);
    
    for i = 1:num_nodes
        path = zeros(1,num_demands);
        for j = 1:num_demands
            % 寻找最近的节点
            diff = location(:,j) - population(:,i,j);
            distance = norm(diff);
            for k = 1:num_nodes
                diff = location(:,j) - population(:,k,j);
                if norm(diff) < distance
                    distance = norm(diff);
                    path(j) = k;
                end
            end
        end
        
        % 计算路径长度
        distance = 0;
        for j = 1:num_demands-1
            diff = population(:,path(j+1),j) - population(:,path(j),j);
            distance = distance + norm(diff);
        end
        
        % 计算适应度值
        fitness(i) = distance;
    end
end

代码中使用了三个函数：

- `updateFish`：更新鱼群位置和速度，实现了个体学习、社会学习和邻域搜索，同时考虑了容量限制。
- `evaluate`：计算鱼群的适应度值，即每个鱼群的路径长度。
- 主函数：初始化数据，迭代更新鱼群位置和速度，并输出最佳解和适应度值。

需要注意的是，代码中的容量限制是简化的，实际应用中可能需要更复杂的容量限制模型。