3维粒子群优化算法MATLAB
时间: 2024-01-24 16:15:31 浏览: 19
以下是一个使用MATLAB实现的3维粒子群优化算法的示例:
```matlab
% 设置参数
numParticles = 50; % 粒子数量
maxIterations = 100; % 最大迭代次数
c1 = 2; % 加速因子c1
c2 = 2; % 加速因子c2
inertiaWeight = 0.9; % 惯性权重
% 初始化粒子位置和速度
positions = rand(numParticles, 3); % 3维空间中的随机位置
velocities = zeros(numParticles, 3); % 初始速度为0
% 初始化个体最优位置和适应值
pBestPositions = positions;
pBestFitness = inf(numParticles, 1);
% 初始化全局最优位置和适应值
gBestPosition = zeros(1, 3);
gBestFitness = inf;
% 迭代优化
for iteration = 1:maxIterations
% 计算适应值
fitness = calculateFitness(positions);
% 更新个体最优位置和适应值
updateIndices = fitness < pBestFitness;
pBestPositions(updateIndices, :) = positions(updateIndices, :);
pBestFitness(updateIndices) = fitness(updateIndices);
% 更新全局最优位置和适应值
[minFitness, minIndex] = min(pBestFitness);
if minFitness < gBestFitness
gBestPosition = pBestPositions(minIndex, :);
gBestFitness = minFitness;
end
% 更新粒子速度和位置
velocities = inertiaWeight * velocities + c1 * rand(numParticles, 3) .* (pBestPositions - positions) + c2 * rand(numParticles, 3) .* (repmat(gBestPosition, numParticles, 1) - positions);
positions = positions + velocities;
end
% 输出最优解
disp('最优解:');
disp(gBestPosition);
function fitness = calculateFitness(positions)
% 计算适应值,这里假设适应值为位置的欧氏距离
fitness = sqrt(sum(positions.^2, 2));
end
```