基于chebyshev、发现这位置更新、参数自适应调整的麻雀搜索算法伪代码 matlab

基于Chebyshev多项式优化的雀鸟觅食（PSO, Particle Swarm Optimization）算法结合了参数自适应调整，可以应用于解决复杂的优化问题。以下是基本的MATLAB实现的伪代码：

% 初始化
function [solution, bestSolution] = chebyshevPSO(nDimensions, bounds, maxIter, w, c1, c2, pBest, gBest)

% 雀鸟粒子数量
numParticles = 50;

% 初始化粒子位置和速度
positions = rand(numParticles, nDimensions, bounds);
velocities = zeros(numParticles, nDimensions);

% 初始最佳解
pBestPositions = positions;
gBestPosition = min(positions, [], 1); % 全局最佳解

for iter = 1:maxIter
    % 更新速度
    velocities = w * velocities + c1 * rand(numParticles, nDimensions) .* (pBestPositions - positions) + c2 * rand(numParticles, nDimensions) .* (gBestPosition - positions);
    
    % 使用Chebyshev多项式逼近约束条件
    u = chebfun(@(x) f(x, ...), positions, 'method', 'griddata'); % 这里假设f()是你需要优化的目标函数
    u_new = chebyshevApproximation(u, bounds); % Chebyshev插值，确保在边界处取最小值
    
    % 更新位置
    newPositions = positions + velocities;
    newPositions = boundChecking(newPositions, bounds); % 确保新位置在给定边界内
    
    % 更新局部最优(pBest)
    for i = 1:numParticles
        if f(newPositions(i, :)) < f(positions(i, :))
            pBestPositions(i, :) = newPositions(i, :);
        end
    end
    
    % 更新全局最优(gBest)
    if f(newPositions(min(1, find(f(newPositions) < f(gBestPosition)))), :) < f(gBestPosition)
        gBestPosition = newPositions(min(1, find(f(newPositions) < f(gBestPosition))));
    end
    
    % 参数自适应调整
    w = w * AdaptiveDecayRate(iter); % 根据迭代次数调整权重
    c1 = c1 * AdaptiveLearningRate(iter); % 自适应学习率
    c2 = c2 * AdaptiveLearningRate(iter); % 自适应社会学习率
    
    % 记录进度
    solution(iter, :) = gBestPosition;
end

bestSolution = gBestPosition; % 最终找到的解决方案

% 辅助函数
function newPositions = boundChecking(positions, bounds)
    newPositions(positions < bounds(:, 1)) = bounds(:, 1);
    newPositions(positions > bounds(:, 2)) = bounds(:, 2);
end