matlab实现了一个基于粒子群优化（PSO）算法的程序，用于寻找一种三层材料结构的最佳配置

clear
clc
%% 参数设置
num_particles = 1000; % 粒子个数
max_iterations = 100; % 最大迭代次数
w = 0.8; % 惯性权重
c1 = 2; % 个体加速因子
c2 = 2; % 社会加速因子
lb = [0,0,0]; % 下界，厚度厚度厚度
ub = [2,2,2]; % 上界% 读取Excel文件
filename = 'mat444.xlsx';
data = readmatrix(filename);% 提取频率及材料参数
frequencies = data(:,1);
material1_mu_real = data(:,2);
material1_mu_imag = data(:,3);
material1_epsilon_real = data(:,4);
material1_epsilon_imag = data(:,5);
material2_mu_real = data(:,6);
material2_mu_imag = data(:,7);
material2_epsilon_real = data(:,8);
material2_epsilon_imag = data(:,9);
material3_mu_real = data(:,10);
material3_mu_imag = data(:,11);
material3_epsilon_real = data(:,12);
material3_epsilon_imag = data(:,13);particles = zeros(num_particles,6);
% 初始化种群(粒子)
for i = 1 : num_particlesmat_types = randperm(3); % 生成1-3的随机排列thickness = 2  * rand(1,3); % 随机生成0-2mm的厚度particles(i,:) = [mat_types,thickness]; % [材料1，材料2，材料3，厚度1，厚度2，厚度3]
endvelocities = zeros(num_particles, 6); % 初始化速度
personal_best_positions = particles; % 记录每个粒子的最佳历史位置
personal_best_fitness = zeros(num_particles, 1); % 适应度初始化% 计算每个粒子的初始适应度
rl_new = zeros(num_particles,length(frequencies));
for i = 1:num_particles[personal_best_fitness(i),rl_new(i,:)] = objective_function(particles(i,:), frequencies, material1_mu_real, material1_mu_imag, material1_epsilon_real, material1_epsilon_imag, material2_mu_real, material2_mu_imag, material2_epsilon_real, material2_epsilon_imag, material3_mu_real, material3_mu_imag, material3_epsilon_real, material3_epsilon_imag);
end[global_best_fitness, best_index] = min(personal_best_fitness);
global_best_position = personal_best_positions(best_index, :);% 收敛性记录
fitness_history = zeros(max_iterations, 1);%% 主循环
rl = zeros(num_particles,length(frequencies));
for iter = 1:max_iterationsfor i = 1:num_particles% 更新速度velocities(i, 1