标准粒子群算法MATLAB代码

以下是一个基本的粒子群算法（PSO）的MATLAB代码示例：

% 参数设置
numParticles = 50;     % 粒子数量
maxIterations = 100;   % 最大迭代次数

% 问题定义
numVariables = 2;      % 变量数量
lowerBounds = [-5, -5]; % 变量下界
upperBounds = [5, 5];   % 变量上界

% 初始化粒子群
particles = struct('position', [], 'velocity', [], 'bestPosition', [], 'bestFitness', []);
for i = 1:numParticles
    particles(i).position = lowerBounds + (upperBounds - lowerBounds) .* rand(1, numVariables);
    particles(i).velocity = zeros(1, numVariables);
    particles(i).bestPosition = particles(i).position;
    particles(i).bestFitness = fitnessFunction(particles(i).position);
end

% 迭代优化
globalBestFitness = inf;
globalBestPosition = [];
for iter = 1:maxIterations
    for i = 1:numParticles
        % 更新粒子速度和位置
        particles(i).velocity = updateVelocity(particles(i).velocity, particles(i).position, particles(i).bestPosition, globalBestPosition);
        particles(i).position = particles(i).position + particles(i).velocity;
        
        % 边界处理
        particles(i).position = max(particles(i).position, lowerBounds);
        particles(i).position = min(particles(i).position, upperBounds);
        
        % 更新个体最佳解和全局最佳解
        fitness = fitnessFunction(particles(i).position);
        if fitness < particles(i).bestFitness
            particles(i).bestPosition = particles(i).position;
            particles(i).bestFitness = fitness;
        end
        if fitness < globalBestFitness
            globalBestFitness = fitness;
            globalBestPosition = particles(i).position;
        end
    end
    
    % 显示当前迭代结果
    disp(['Iteration ', num2str(iter), ': Best Fitness = ', num2str(globalBestFitness)]);
end

% 结果显示
disp('Optimization finished.');
disp(['Best Fitness = ', num2str(globalBestFitness)]);
disp(['Best Position = [', num2str(globalBestPosition), ']']);

% 适应度函数示例
function fitness = fitnessFunction(position)
    % 替换为你自己的适应度函数计算
    x1 = position(1);
    x2 = position(2);
    fitness = x1^2 + x2^2;
end

% 随机性权重设置
function updatedVelocity = updateVelocity(velocity, currentPosition, bestPosition, globalBestPosition)
    % 这里使用了标准粒子群算法的更新公式，可以根据需要进行修改
    inertiaWeight = 0.5;
    cognitiveWeight = 1;
    socialWeight = 1;
    
    cognitiveComponent = cognitiveWeight * rand(1) * (bestPosition - currentPosition);
    socialComponent = socialWeight * rand(1) * (globalBestPosition - currentPosition);
    
    updatedVelocity = inertiaWeight * velocity + cognitiveComponent + socialComponent;
end

这是一个简单的粒子群算法的实现，其中定义了适应度函数和粒子更新公式。你可以根据自己的问题进行适当的修改和优化。