写一段matlab群智能算法代码，其中种群分为两类

好的，以下是一段简单的 Matlab 群智能算法代码，其中种群分为两类：

function [bestSol, bestFitness] = PSO(populationSize, lowerBound1, upperBound1, lowerBound2, upperBound2)
% populationSize: 种群大小
% lowerBound1, upperBound1: 种群1的取值范围
% lowerBound2, upperBound2: 种群2的取值范围

% 初始化种群1和种群2
pop1 = randi([lowerBound1, upperBound1], [populationSize, 1]);
pop2 = randi([lowerBound2, upperBound2], [populationSize, 1]);

% 初始化速度
velocity1 = zeros(populationSize, 1);
velocity2 = zeros(populationSize, 1);

% 计算每个个体的适应度值
fitness = calculateFitness(pop1, pop2);

% 初始化最优位置和适应度值
bestPos1 = pop1;
bestPos2 = pop2;
bestFitness = fitness;

% 循环迭代
for i = 1:1000
    % 更新速度和位置
    w = 0.8 - 0.7 * i / 1000;  % 惯性权重
    c1 = 2;  % 个体认知系数
    c2 = 2;  % 群体社会系数
    r1 = rand(populationSize, 1);
    r2 = rand(populationSize, 1);
    velocity1 = w * velocity1 + c1 * r1 .* (bestPos1 - pop1) + c2 * r2 .* (bestPos1 - pop1);
    velocity2 = w * velocity2 + c1 * r1 .* (bestPos2 - pop2) + c2 * r2 .* (bestPos2 - pop2);
    pop1 = pop1 + velocity1;
    pop2 = pop2 + velocity2;
    
    % 限制位置在取值范围内
    pop1(pop1 < lowerBound1) = lowerBound1;
    pop1(pop1 > upperBound1) = upperBound1;
    pop2(pop2 < lowerBound2) = lowerBound2;
    pop2(pop2 > upperBound2) = upperBound2;
    
    % 计算每个个体的适应度值
    fitness = calculateFitness(pop1, pop2);
    
    % 更新最优位置和适应度值
    replaceIdx = fitness < bestFitness;
    bestPos1(replaceIdx, 1) = pop1(replaceIdx, 1);
    bestPos2(replaceIdx, 1) = pop2(replaceIdx, 1);
    bestFitness(replaceIdx, 1) = fitness(replaceIdx, 1);
end

% 返回适应度最优的个体和适应度值
[bestFitness, bestIdx] = min(bestFitness);
bestSol = [bestPos1(bestIdx, 1), bestPos2(bestIdx, 1)];
end

function fitness = calculateFitness(pop1, pop2)
% 计算每个个体的适应度值
fitness = abs(pop1 .* sin(pop2) - 1);
end

其中，种群1的取值范围为 [lowerBound1, upperBound1]，种群2的取值范围为 [lowerBound2, upperBound2]，种群大小为 populationSize。calculateFitness 函数是适应度函数，用于计算每个个体的适应度值。PSO 函数是粒子群优化算法的主函数，用于进行迭代优化，最终返回适应度最优的个体和适应度值。

需要注意的是，本算法代码中并没有判断种群大小是否符合要求，需要在调用 PSO 函数之前对种群大小进行判断。