主程序 设置搜索范围和ACO参数bounds = -10 -10; 10 10;na = 100; 蚂蚁数maxiter = 100; 迭代次数rho = 01; 信息素挥发率adapt_rate = 01; 自适应因子 运行ACO算法求解Shubert函数的最小值xopt yopt fopt = ACOShubert na maxiter bounds rho adapt_rate;

% 主程序 % 设置搜索范围和ACO参数 bounds = [-10, -10; 10, 10]; na = 100; % 蚂蚁数 maxiter = 100; % 迭代次数 rho = 0.1; % 信息素挥发率 adapt_rate = 0.1; % 自适应因子

% 运行ACO算法求解Shubert函数的最小值 [xopt, yopt, fopt] = ACO(@Shubert, na, maxiter, bounds, rho, adapt_rate);

% 输出结果 fprintf('最优解：(%f, %f)\n', xopt, yopt); fprintf('最小值：%.4f\n', fopt);

% Shubert函数 % 输入：x和y向量 % 输出：函数值f function f = Shubert(x, y) A = 0; B = 0; for i = 1:5 A = A + i * cos((i + 1) * x + i); B = B + i * cos((i + 1) * y + i); end f = A * B; end

%优化算法 function [xopt, yopt, fopt] = ACO(objfun, na, maxiter, bounds, rho, adapt_rate) % 初始化信息素和启发式函数权重 tau = ones(size(bounds,1), na); beta = ones(size(bounds,1), na) * 0.5; bestf = inf; bestx = zeros(size(bounds,1), 1); besty = zeros(size(bounds,1), 1);

% 迭代优化
for iter = 1:maxiter
    % 初始化蚂蚁位置
    antx = rand(na, 1) * (bounds(2, 1) - bounds(1, 1)) + bounds(1, 1);
    anty = rand(na, 1) * (bounds(2, 2) - bounds(1, 2)) + bounds(1, 2);
    % 计算蚂蚁适应度和最优解
    f = objfun(antx, anty);
    [minf, minidx] = min(f);
    if minf < bestf
        bestf = minf;
        bestx = antx(minidx);
        besty = anty(minidx);
    end
    % 更新信息素和启发式函数权重
    delta_tau = zeros(size(tau));
    for i = 1:na
        for j = 1:size(bounds,1)
            delta_tau(j,i) = 1/f(i) * (bestf/f(i))^adapt_rate;
            tau(j,i) = (1 - rho) * tau(j,i) + rho * delta_tau(j,i);
            beta(j,i) = tau(j,i)^(0.5);
        end
    end
end
% 返回最优解和最小值
xopt = bestx;
yopt = besty;
fopt = bestf;

en