三维装箱算法matlab,三维装箱遗传算法matlab程序

三维装箱算法的 Matlab 程序可能因为算法的具体实现方式不同而有所差异，这里提供一种基于贪心思想的三维装箱算法的 Matlab 实现：

function [bin,bin_num] = packing_3d(item,dim)
% item: 物品列表，每个物品的尺寸为 [w,h,d]
% dim: 装箱区域的尺寸，[W,H,D]

n = size(item,1);
item = sortrows(item,-1); % 将物品按照高度从大到小排序

bin = zeros(dim);
bin_num = 1;

for i = 1:n
    [bin,success] = put_item(item(i,:),bin,dim); % 尝试将当前物品放入箱子中
    if ~success % 放置失败，启用新的箱子
        bin_num = bin_num + 1;
        bin(:,:,bin_num) = zeros(dim);
        [bin(:,:,bin_num),~] = put_item(item(i,:),bin(:,:,bin_num),dim);
    end
end

end

function [bin,success] = put_item(item,bin,dim)
% 尝试将物品放入箱子中

w = item(1); h = item(2); d = item(3);

% 生成所有可能的放置方案
place = [w,h,d; w,d,h; h,w,d; h,d,w; d,w,h; d,h,w];

for i = 1:size(place,1)
    [bin,success] = place_item(place(i,:),bin,dim);
    if success
        return
    end
end

success = false;

end

function [bin,success] = place_item(item,bin,dim)
% 尝试将物品按照指定方向放入箱子中

w = item(1); h = item(2); d = item(3);

for x = 1:dim(1)-w+1
    for y = 1:dim(2)-h+1
        for z = 1:dim(3)-d+1
            if all(all(all(bin(x:x+w-1,y:y+h-1,z:z+d-1)==0)))
                bin(x:x+w-1,y:y+h-1,z:z+d-1) = 1;
                success = true;
                return
            end
        end
    end
end

success = false;

end

三维装箱遗传算法的 Matlab 程序也有多种实现方式，这里提供一种基于遗传算法的三维装箱算法的 Matlab 实现：

function [best_fit,boxes] = packing_3d_ga(item,dim,pop_size,gen_num,mut_rate,keep_rate)
% item: 物品列表，每个物品的尺寸为 [w,h,d]
% dim: 装箱区域的尺寸，[W,H,D]
% pop_size: 种群大小
% gen_num: 迭代次数
% mut_rate: 变异率
% keep_rate: 保留率

n = size(item,1);
item = sortrows(item,-1); % 将物品按照高度从大到小排序

pop = randi([1,dim(1)-max(item(:,1))+1],pop_size,n); % 随机生成种群

for i = 1:gen_num
    % 计算每个个体的适应度
    fit = zeros(1,pop_size);
    for j = 1:pop_size
        boxes = zeros(dim);
        for k = 1:n
            [boxes,success] = place_item(item(k,:),boxes,pop(j,k),dim);
            if ~success % 放置失败，当前个体适应度为 0
                fit(j) = 0;
                break
            end
        end
        if fit(j) ~= 0 % 放置成功，计算适应度
            fit(j) = calc_fit(boxes,dim);
        end
    end
    
    % 选择、交叉、变异
    [~,idx] = sort(fit,'descend');
    keep_num = round(keep_rate*pop_size);
    new_pop = zeros(pop_size,n);
    new_pop(1:keep_num,:) = pop(idx(1:keep_num),:);
    for j = keep_num+1:pop_size
        p1 = pop(idx(randi(keep_num)),:);
        p2 = pop(idx(randi(keep_num)),:);
        c = crossover(p1,p2);
        new_pop(j,:) = mutation(c,mut_rate,dim);
    end
    pop = new_pop;
end

% 找出最佳方案
best_fit = 0;
for i = 1:pop_size
    boxes = zeros(dim);
    for j = 1:n
        [boxes,success] = place_item(item(j,:),boxes,pop(i,j),dim);
        if ~success % 放置失败，当前个体适应度为 0
            break
        end
    end
    if success % 放置成功，计算适应度
        fit = calc_fit(boxes,dim);
        if fit > best_fit
            best_fit = fit;
            boxes_best = boxes;
        end
    end
end

boxes = boxes_best;

end

function [boxes,success] = place_item(item,boxes,x,dim)
% 将物品按照指定方向放入箱子中

w = item(1); h = item(2); d = item(3);

if x+w-1 > dim(1) || any(any(any(boxes(x:x+w-1,:,1:d)==1)))
    success = false;
    return
end

boxes(x:x+w-1,:,1:d) = 1;
success = true;

end

function fit = calc_fit(boxes,dim)
% 计算适应度

fit = sum(sum(sum(boxes,3)>0))/dim(1)/dim(2);

end

function c = crossover(p1,p2)
% 交叉

n = length(p1);
c = zeros(1,n);
k = randi(n);
c(1:k) = p1(1:k);
for i = k+1:n
    idx = find(~ismember(p2,c));
    c(i) = p2(idx(randi(length(idx))));
end

end

function c = mutation(p,rate,dim)
% 变异

n = length(p);
c = p;
if rand < rate
    k = randi(n);
    c(k) = randi([1,dim(1)-max(p(:,1))+1]);
end

end

请注意，这两个程序可能只是三维装箱问题的一些基础实现，具体的应用场景和需求可能需要更加复杂的算法和实现方式。