1% 初始化种群 2function population = initialize_population(pop_size, dim, lb, ub) 3 population = zeros(pop_size, dim); 4 for i = 1:pop_size 5 population(i,:) = lb + (ub - lb).* rand(1, dim); 6 end 7end 8 9% 评估适应度 10function fitness = evaluate_fitness(population, model) 11 [pop_size, ~] = size(population); 12 fitness = zeros(pop_size, 1); 13 for i = 1:pop_size 14 path = decode_population(population(i,:), model); 15 fitness(i) = calculate_objective_function(path, model); 16 end 17end 18 19% 觅食阶段 20function new_population = foraging_stage(population, best_solution, model) 21 [pop_size, dim] = size(population); 22 new_population = population; 23 for i = 1:pop_size 24 tau1 = normrnd(0, 1); 25 tau2 = rand(); 26 r = randi([1, pop_size]); 27 y = (population(i,:) + population(r,:)) / 2; 28 new_position = population(i,:) + tau1 * sqrt(2) * tau2 * (best_solution - y); 29 % 边界处理 30 new_position = max(new_position, model.lb); 31 new_position = min(new_position, model.ub); 32 % 评估新位置 33 new_path = decode_population(new_position, model); 34 new_fitness = calculate_objective_function(new_path, model); 35 old_path = decode_population(population(i,:), model); 36 old_fitness = calculate_objective_function(old_path, model); 37 if new_fitness < old_fitness 38 new_population(i,:) = new_position; 39 end 40 end 41end 42 43% 群体协作阶段 44function new_population = group_collaboration_stage(population, best_solution, model) 45 [pop_size, dim] = size(population); 46 new_population = population; 47 for i = 1:pop_size 48 U1 = randi([0, 1], 1, dim); 49 r1 = randi([1, pop_size]); 50 r2 = randi([1, pop_size]); 51 tau3 = rand(); 52 y = (population(i,:) + best_solution) / 2; 53 new_position = (1 - U1).* population(i,:) + U1.* (y + tau3 * (population(r1,:) - population(r2,:))); 54 % 边界处理 55 new_position = max(new_position, model.lb); 56 new_position = min(new_position, model.ub); 57 % 评估新位置 58 new_path = decode_population(new_position, model); 59 new_fitness = calculate_objective_function(new_path, model); 60 old_path = decode_population(population(i,:), model); 61 old_fitness = calculate_objective_function(old_path, model); 62 if new_fitness < old_fitness 63 new_population(i,:) = new_position; 64 end 65 end 66end 67 68% 自卫阶段 69function new_population = self_defense_stage(population, model) 70 [pop_size, dim] = size(population); 71 new_population = population; 72 for i = 1:pop_size 73 % 判断是否陷入局部最优(简单示例,可根据实际情况调整) 74 if rand() < 0.2 75 tau4 = rand(); 76 Fti = calculate_average_force(population, i); % 自定义函数,计算平均力 77 alpha = 0.1; % 收敛速度因子 78 new_position = population(i,:) + alpha * tau4 * Fti; 79 % 边界处理 80 new_position = max(new_position, model.lb); 81 new_position = min(new_position, model.ub); 82 % 评估新位置 83 new_path = decode_population(new_position, model); 84 new_fitness = calculate_objective_function(new_path, model); 85 old_path = decode_population(population(i,:), model); 86 old_fitness = calculate_objective_function(old_path, model); 87 if new_fitness < old_fitness 88 new_population(i,:) = new_position; 89 end 90 end 91 end 92end 93 94% CPO 算法主函数 95function [best_solution, best_fitness, convergence_curve] = CPO(pop_size, max_iter, dim, lb, ub, model) 96 population = initialize_population(pop_size, dim, lb, ub); 97 fitness = evaluate_fitness(population, model); 98 [best_fitness, best_idx] = min(fitness); 99 best_solution = population(best_idx,:); 100 convergence_curve = zeros(max_iter, 1); 101 T = 2; % 循环次数 102 T_max = max_iter; 103 N_min = round(pop_size / 3); % 最小种群大小 104 for iter = 1:max_iter 105 % 更新种群大小 106 N = N_min + (pop_size - N_min) * (1 - ((mod(iter, T_max / T) / (T_max / T)))); 107 if N < pop_size 108 % 随机选择个体保留 109 idx = randperm(pop_size, N); 110 population = population(idx,:); 111 fitness = fitness(idx); 112 else 113 % 恢复种群大小 114 % 这里可以简单复制部分个体或重新初始化部分个体,示例中简单处理 115 if size(population,
## 实验结果
