基于麻雀搜索算法(SSA)的三维旅行商问题 matlab代码

以下是基于麻雀搜索算法(SSA)的三维旅行商问题的 MATLAB 代码：

function [bestPath, bestDist] = ssa_3dtsp(cityPos, n, maxIter, numBirds)
% cityPos: 城市坐标矩阵
% n: 城市数量
% maxIter: 最大迭代次数
% numBirds: 麻雀数量

% 初始化麻雀的位置和速度
pos = 100 * rand(numBirds, n, 3); % 位置
vel = rand(numBirds, n, 3); % 速度

% 计算每只麻雀的适应值
dist = zeros(numBirds, maxIter); % 距离
for i = 1:numBirds
    dist(i, 1) = calDist(pos(i, :, :), n);
end

% 初始化全局最优解
[bestPath, bestDist] = getBestPath(pos, dist, n);

for k = 2:maxIter
    % 飞行模拟
    for i = 1:numBirds
        vel(i, :, :) = vel(i, :, :) + ssai(pos(i, :, :), dist(i, k-1), bestPath, n);
        pos(i, :, :) = pos(i, :, :) + vel(i, :, :);
        pos(i, :, :) = max(min(pos(i, :, :), 100), 0); % 确保位置在合法范围内
        dist(i, k) = calDist(pos(i, :, :), n);
    end
    
    % 群体行为
    [bestPath, bestDist] = getBestPath(pos, dist, n);
    for i = 1:numBirds
        vel(i, :, :) = vel(i, :, :) + ssaq(pos(i, :, :), bestPath, n);
        pos(i, :, :) = pos(i, :, :) + vel(i, :, :);
        pos(i, :, :) = max(min(pos(i, :, :), 100), 0); % 确保位置在合法范围内
        dist(i, k) = calDist(pos(i, :, :), n);
    end
    
    % 局部搜索
    randBirds = randperm(numBirds, round(numBirds/2));
    for i = 1:length(randBirds)
        pos(randBirds(i), :, :) = localSearch(pos(randBirds(i), :, :), n);
        dist(randBirds(i), k) = calDist(pos(randBirds(i), :, :), n);
    end
    
    % 终止条件
    if k >= 50 && std(dist(:, k-49:k)) < 1e-6
        break;
    end
end

% 输出结果
bestPath = [bestPath, bestPath(:, 1)];
fprintf('Best path: %s\n', num2str(bestPath));
fprintf('Best distance: %.4f\n', bestDist);

% 计算路径长度
function dist = calDist(pos, n)
dist = 0;
for i = 1:n-1
    dist = dist + norm(pos(i, :) - pos(i+1, :));
end
dist = dist + norm(pos(n, :) - pos(1, :));

% 获取全局最优解
function [bestPath, bestDist] = getBestPath(pos, dist, n)
[bestDist, idx] = min(dist(:, end));
bestPath = squeeze(pos(idx, :, :))';
[~, bestPath] = sortrows(bestPath);

% 麻雀飞行策略
function vel = ssai(pos, dist, bestPath, n)
w = 0.5; % 惯性权重
c1 = 1.5; % 个体学习因子
c2 = 1.5; % 社会学习因子
r1 = rand(1, 3);
r2 = rand(1, 3);
r3 = rand(1, 3);
vel = w * squeeze(pos) + c1 * r1 .* (squeeze(pos) - repmat(pos(:, :, 1), 1, 1, 3)) + ...
    c2 * r2 .* (repmat(bestPath', size(pos, 1), 1, 1) - pos) + ...
    c2 * r3 .* (repmat(pos(1, :, :), size(pos, 1), n, 1) - pos);

% 麻雀群体行为策略
function vel = ssaq(pos, bestPath, n)
w = 0.5; % 惯性权重
c3 = 1.5; % 群体学习因子
r4 = rand(1, 3);
vel = w * squeeze(pos) + c3 * r4 .* (repmat(bestPath', size(pos, 1), 1, 1) - pos);

% 局部搜索
function newPos = localSearch(pos, n)
newPos = pos;
for i = 1:n-1
    for j = i+1:n
        if j - i > 1
            newOrder = [pos(1:i-1, :); pos(i:j-1, :); pos(j:end, :)];
            newDist = calDist(newOrder, n);
            oldDist = calDist(pos, n);
            if newDist < oldDist
                newPos = newOrder;
            end
        end
    end
end

注意：此代码仅供参考，实际应用中可能需要根据具体情况进行修改。