使用matlab编写pso算法，适应度函数为：f=0.5/((100.0y)/(0.1/y + 50.0)^2 + 10.0xz^2 - 100.0xz^3) + (0.5(0.037/(x*y)^(1/2))^(1/2))/z^(1/2)

这里是一个简单的PSO算法的Matlab代码，适应度函数已经被定义为f，参数设置如下：

- 粒子数目：particleNum
- 迭代次数：iterMax
- 惯性权重：w
- 学习因子：c1、c2

% 定义适应度函数
f = @(x,y,z) 0.5/((100.0*y)/(0.1/y + 50.0)^2 + 10.0*x*z^2 - 100.0*x*z^3) + (0.5*(0.037/(x*y)^(1/2))^(1/2))/z^(1/2);

% 初始化PSO算法参数
particleNum = 50; % 粒子数目
iterMax = 100; % 迭代次数
w = 0.8; % 惯性权重
c1 = 2.0; % 学习因子1
c2 = 2.0; % 学习因子2

% 初始化粒子位置和速度
x = rand(particleNum, 1)*10;
y = rand(particleNum, 1)*10;
z = rand(particleNum, 1)*10;
vx = rand(particleNum, 1)*10 - 5;
vy = rand(particleNum, 1)*10 - 5;
vz = rand(particleNum, 1)*10 - 5;

% 初始化粒子最优位置和全局最优位置
pBestX = x;
pBestY = y;
pBestZ = z;
pBestVal = f(x, y, z);
gBestVal = min(pBestVal);
gBestIndex = find(pBestVal == gBestVal);

% 开始迭代
for iter = 1:iterMax
    % 更新粒子速度和位置
    for i = 1:particleNum
        vx(i) = w*vx(i) + c1*rand()*(pBestX(i)-x(i)) + c2*rand()*(pBestX(gBestIndex)-x(i));
        vy(i) = w*vy(i) + c1*rand()*(pBestY(i)-y(i)) + c2*rand()*(pBestY(gBestIndex)-y(i));
        vz(i) = w*vz(i) + c1*rand()*(pBestZ(i)-z(i)) + c2*rand()*(pBestZ(gBestIndex)-z(i));
        x(i) = x(i) + vx(i);
        y(i) = y(i) + vy(i);
        z(i) = z(i) + vz(i);
        
        % 边界处理
        x(i) = min(max(x(i), 0), 10);
        y(i) = min(max(y(i), 0), 10);
        z(i) = min(max(z(i), 0), 10);
        
        % 更新最优位置
        val = f(x(i), y(i), z(i));
        if val < pBestVal(i)
            pBestX(i) = x(i);
            pBestY(i) = y(i);
            pBestZ(i) = z(i);
            pBestVal(i) = val;
        end
        if val < gBestVal
            gBestVal = val;
            gBestIndex = i;
        end
    end
    
    % 输出迭代结果
    fprintf("Iteration %d: gBestVal = %f, gBestX = %f, gBestY = %f, gBestZ = %f\n", iter, gBestVal, pBestX(gBestIndex), pBestY(gBestIndex), pBestZ(gBestIndex));
end