生成一种基于Tent混沌映射初始化种群的改进粒子群算法的matlab代码

以下是基于Tent混沌映射初始化种群的改进粒子群算法的MATLAB代码：

function [gbest, gbestFit, iter] = TentPSO(fitnessFunc, dim, lb, ub, maxIter, popSize, c1, c2, w)
% TentPSO: Improved Particle Swarm Optimization algorithm using Tent map initialization
% Inputs:
% fitnessFunc: function handle for the fitness function
% dim: number of dimensions
% lb: lower bounds of the search space, a vector of length dim
% ub: upper bounds of the search space, a vector of length dim
% maxIter: maximum number of iterations
% popSize: population size
% c1: cognitive parameter
% c2: social parameter
% w: inertia weight
% Outputs:
% gbest: global best solution
% gbestFit: fitness value of global best solution
% iter: number of iterations executed

% Initialize population using Tent map
rng('shuffle');
pop = zeros(popSize, dim);
for i = 1:popSize
    pop(i, :) = lb + (ub - lb) * TentMap(rand(), dim);
end

% Initialize velocities
vel = zeros(popSize, dim);
pbest = pop;
pbestFit = zeros(popSize, 1);
for i = 1:popSize
    pbestFit(i) = fitnessFunc(pbest(i, :));
end

% Initialize global best
[gbestFit, gbestIdx] = min(pbestFit);
gbest = pbest(gbestIdx, :);

% Run PSO iterations
for iter = 1:maxIter
    % Update velocities and positions
    vel = w * vel + c1 * rand(popSize, dim) .* (pbest - pop) ...
        + c2 * rand(popSize, dim) .* (gbest - pop);
    pop = pop + vel;
    
    % Check for out-of-bounds positions and correct them
    for i = 1:popSize
        pop(i, :) = max(pop(i, :), lb);
        pop(i, :) = min(pop(i, :), ub);
    end
    
    % Evaluate fitness and update personal best
    for i = 1:popSize
        fit = fitnessFunc(pop(i, :));
        if fit < pbestFit(i)
            pbest(i, :) = pop(i, :);
            pbestFit(i) = fit;
        end
    end
    
    % Update global best
    [minFit, minIdx] = min(pbestFit);
    if minFit < gbestFit
        gbestFit = minFit;
        gbest = pbest(minIdx, :);
    end
    
    % Update inertia weight
    w = w * 0.99;
end

end

function x = TentMap(x0, n)
% TentMap: Generate n Tent map values starting from x0
% Inputs:
% x0: initial value for Tent map
% n: number of values to generate
% Outputs:
% x: a vector of Tent map values

x = zeros(n, 1);
x(1) = x0;
for i = 2:n
    if x(i-1) < 0.5
        x(i) = 2 * x(i-1);
    else
        x(i) = 2 - 2 * x(i-1);
    end
end

end

其中，`TentPSO`函数是主函数，接受上述输入参数并返回最优解、最优解的适应度值和迭代次数。`TentMap`函数用于生成Tent混沌映射序列，用于初始化种群。在`TentPSO`函数中，首先使用Tent混沌映射生成初始种群，然后按照标准粒子群算法的方式更新速度和位置，计算适应度值，更新个体最优解和全局最优解。最后，按照迭代次数递减的方式更新惯性权重。