使用生物免疫算法和遗传算法实现动态信道分配
1. 设计
1.1 动态信道分配问题描述
classdef DynamicChannelAllocation
% 动态信道分配问题建模
properties
num_cells % 小区数量
num_channels % 可用信道数量
num_users % 用户数量
interference_map % 干扰矩阵
demand_matrix % 需求矩阵
cell_layout % 小区布局
user_positions % 用户位置
system_config % 系统配置
end
methods
function obj = DynamicChannelAllocation(config)
% 初始化动态信道分配问题
obj.num_cells = config.num_cells;
obj.num_channels = config.num_channels;
obj.num_users = config.num_users;
obj.system_config = config;
% 生成网络拓扑和干扰模型
obj = generate_network_topology(obj);
obj = generate_interference_map(obj);
obj = generate_demand_matrix(obj);
end
function obj = generate_network_topology(obj)
% 生成网络拓扑结构
fprintf('生成网络拓扑...\n');
% 生成小区布局（六边形蜂窝网络）
obj.cell_layout = zeros(obj.num_cells, 2);
radius = 1; % 小区半径
% 六边形布局坐标计算
k = 1;
for ring = 0:ceil(sqrt(obj.num_cells)/2)
for i = 0:6*ring-1
if k > obj.num_cells, break; end
angle = 2*pi*i/(6*max(ring,1));
obj.cell_layout(k, :) = [ring*cos(angle), ring*sin(angle)];
k = k + 1;
end
end
% 生成用户位置
obj.user_positions = zeros(obj.num_users, 2);
for i = 1:obj.num_users
cell_id = randi(obj.num_cells);
angle = 2*pi*rand();
dist = radius * sqrt(rand());
obj.user_positions(i, :) = obj.cell_layout(cell_id, :) + ...
dist * [cos(angle), sin(angle)];
end
end
function obj = generate_interference_map(obj)
% 生成干扰矩阵
fprintf('生成干扰矩阵...\n');
obj.interference_map = zeros(obj.num_cells, obj.num_cells);
for i = 1:obj.num_cells
for j = 1:obj.num_cells
if i == j
obj.interference_map(i, j) = 1.0; % 同小区干扰
else
% 计算小区间距离
dist = norm(obj.cell_layout(i, :) - obj.cell_layout(j, :));
% 距离越近，干扰越大
obj.interference_map(i, j) = max(0, 1 - dist/3);
end
end
end
end
function obj = generate_demand_matrix(obj)
% 生成用户需求矩阵
fprintf('生成需求矩阵...\n');
obj.demand_matrix = zeros(obj.num_cells, obj.num_channels);
% 每个小区的用户数量分布
users_per_cell = randi([1, 5], obj.num_cells, 1);
total_users = sum(users_per_cell);
% 调整用户数量
if total_users ~= obj.num_users
scale_factor = obj.num_users / total_users;
users_per_cell = max(1, round(users_per_cell * scale_factor));
end
user_count = 0;
for cell = 1:obj.num_cells
num_cell_users = min(users_per_cell(cell), obj.num_users - user_count);
for ch = 1:obj.num_channels
% 随机生成信道需求
if rand() < 0.3 % 30%的概率需要该信道
obj.demand_matrix(cell, ch) = ...
randi([1, num_cell_users]); % 需求用户数
end
end
user_count = user_count + num_cell_users;
if user_count >= obj.num_users, break; end
end
end
function fitness = evaluate_allocation(obj, allocation)
% 评估信道分配方案的适应度
% allocation: num_cells x num_channels 矩阵，表示信道分配
total_interference = 0;
total_satisfaction = 0;
total_penalty = 0;
% 计算干扰
for ch = 1:obj.num_channels
for i = 1:obj.num_cells
if allocation(i, ch) > 0 % 如果小区i使用了信道ch
for j = 1:obj.num_cells
if i ~= j && allocation(j, ch) > 0
% 累积干扰
total_interference = total_interference + ...
obj.interference_map(i, j) * allocation(i, ch) * allocation(j, ch);
end
end
end
end
end
% 计算需求满足度
for i = 1:obj.num_cells
for ch = 1:obj.num_channels
if allocation(i, ch) > 0
% 计算需求满足程度
satisfaction = min(allocation(i, ch), obj.demand_matrix(i, ch));
total_satisfaction = total_satisfaction + satisfaction;
% 计算惩罚（过度分配或分配不足）
if allocation(i, ch) > obj.demand_matrix(i, ch)
penalty = (allocation(i, ch) - obj.demand_matrix(i, ch))^2;
else
penalty = (obj.demand_matrix(i, ch) - allocation(i, ch))^2;
end
total_penalty = total_penalty + penalty;
end
end
end
% 计算总适应度（最大化）
max_possible_satisfaction = sum(obj.demand_matrix(:));
satisfaction_ratio = total_satisfaction / max_possible_satisfaction;
% 适应度函数
fitness = satisfaction_ratio - 0.5 * (total_interference / (obj.num_cells^2)) ...
- 0.1 * (total_penalty / max_possible_satisfaction);
% 确保适应度为非负
fitness = max(0, fitness);
end
end
end
2. 遗传算法实现
2.1 遗传算法核心类
classdef GeneticAlgorithm
% 遗传算法实现动态信道分配
properties
population_size % 种群大小
max_generations % 最大迭代次数
crossover_rate % 交叉概率
mutation_rate % 变异概率
elitism_count % 精英保留数量
problem % 信道分配问题实例
population % 种群
fitness % 适应度值
best_solution % 最优解
best_fitness % 最优适应度
convergence % 收敛曲线
end
methods
function ga = GeneticAlgorithm(problem, config)
% 初始化遗传算法
ga.problem = problem;
ga.population_size = config.population_size;
ga.max_generations = config.max_generations;
ga.crossover_rate = config.crossover_rate;
ga.mutation_rate = config.mutation_rate;
ga.elitism_count = config.elitism_count;
ga.convergence = zeros(config.max_generations, 1);
end
function ga = initialize_population(ga)
% 初始化种群
fprintf('初始化遗传算法种群...\n');
num_cells = ga.problem.num_cells;
num_channels = ga.problem.num_channels;
ga.population = zeros(ga.population_size, num_cells, num_channels);
ga.fitness = zeros(ga.population_size, 1);
for i = 1:ga.population_size
% 随机生成信道分配方案
solution = zeros(num_cells, num_channels);
for cell = 1:num_cells
for ch = 1:num_channels
if rand() < 0.4 % 40%的概率分配信道
max_users = ga.problem.demand_matrix(cell, ch);
if max_users > 0
solution(cell, ch) = randi([0, max_users]);
end
end
end
end
ga.population(i, :, :) = solution;
ga.fitness(i) = ga.problem.evaluate_allocation(solution);
end
% 记录最优解
[ga.best_fitness, best_idx] = max(ga.fitness);
ga.best_solution = squeeze(ga.population(best_idx, :, :));
end
function ga = evolve(ga)
% 执行遗传算法进化
fprintf('开始遗传算法进化...\n');
for gen = 1:ga.max_generations
% 选择
selected_indices = ga.selection();
% 交叉
new_population = ga.crossover(selected_indices);
% 变异
new_population = ga.mutation(new_population);
% 评估新种群
new_fitness = zeros(ga.population_size, 1);
for i = 1:ga.population_size
new_fitness(i) = ga.problem.evaluate_allocation(...
squeeze(new_population(i, :, :)));
end
% 精英保留
[ga.population, ga.fitness] = ga.elitism(...
ga.population, ga.fitness, new_population, new_fitness);
% 更新最优解
[current_best_fitness, best_idx] = max(ga.fitness);
if current_best_fitness > ga.best_fitness
ga.best_fitness = current_best_fitness;
ga.best_solution = squeeze(ga.population(best_idx, :, :));
end
ga.convergence(gen) = ga.best_fitness;
if mod(gen, 10) == 0
fprintf('代数 %d: 最优适应度 = %.4f\n', gen, ga.best_fitness);
end
end
end
function selected_indices = selection(ga)
% 轮盘赌选择
fitness_sum = sum(ga.fitness);
if fitness_sum == 0
% 如果所有适应度都为0，均匀选择
selected_indices = randi(ga.population_size, ga.population_size, 1);
else
probabilities = ga.fitness / fitness_sum;
cumulative_prob = cumsum(probabilities);
selected_indices = zeros(ga.population_size, 1);
for i = 1:ga.population_size
r = rand();
selected_indices(i) = find(cumulative_prob >= r, 1);
end
end
end
function new_population = crossover(ga, selected_indices)
% 单点交叉
num_cells = ga.problem.num_cells;
num_channels = ga.problem.num_channels;
new_population = zeros(ga.population_size, num_cells, num_channels);
for i = 1:2:ga.population_size
parent1_idx = selected_indices(i);
parent2_idx = selected_indices(min(i+1, ga.population_size));
parent1 = squeeze(ga.population(parent1_idx, :, :));
parent2 = squeeze(ga.population(parent2_idx, :, :));
if rand() < ga.crossover_rate
% 执行交叉
crossover_point = randi([1, num_cells-1]);
child1 = [parent1(1:crossover_point, :);
parent2(crossover_point+1:end, :)];
child2 = [parent2(1:crossover_point, :);
parent1(crossover_point+1:end, :)];
else
% 不交叉，直接复制
child1 = parent1;
child2 = parent2;
end
new_population(i, :, :) = child1;
if i+1 <= ga.population_size
new_population(i+1, :, :) = child2;
end
end
end
function population = mutation(ga, population)
% 变异操作
num_cells = ga.problem.num_cells;
num_channels = ga.problem.num_channels;
for i = 1:ga.population_size
individual = squeeze(population(i, :, :));
for cell = 1:num_cells
for ch = 1:num_channels
if rand() < ga.mutation_rate
% 随机变异
max_users = ga.problem.demand_matrix(cell, ch);
if max_users > 0
individual(cell, ch) = randi([0, max_users]);
else
individual(cell, ch) = 0;
end
end
end
end
population(i, :, :) = individual;
end
end
function [new_population, new_fitness] = elitism(ga, old_pop, old_fit, new_pop, new_fit)
% 精英保留策略
[~, sorted_indices] = sort(old_fit, 'descend');
elite_indices = sorted_indices(1:ga.elitism_count);
% 用精英个体替换新种群中最差的个体
[~, worst_indices] = sort(new_fit, 'ascend');
replace_indices = worst_indices(1:ga.elitism_count);
new_population = new_pop;
new_fitness = new_fit;
for i = 1:ga.elitism_count
new_population(replace_indices(i), :, :) = ...
old_pop(elite_indices(i), :, :);
new_fitness(replace_indices(i)) = old_fit(elite_indices(i));
end
end
function plot_convergence(ga)
% 绘制收敛曲线
figure;
plot(1:ga.max_generations, ga.convergence, 'b-', 'LineWidth', 2);
xlabel('进化代数');
ylabel('最优适应度');
title('遗传算法收敛曲线');
grid on;
end
function visualize_allocation(ga)
% 可视化信道分配结果
figure('Position', [100, 100, 1200, 800]);
% 子图1: 网络拓扑
subplot(2, 3, 1);
scatter(ga.problem.cell_layout(:, 1), ga.problem.cell_layout(:, 2), ...
100, 'filled', 'b');
hold on;
scatter(ga.problem.user_positions(:, 1), ga.problem.user_positions(:, 2), ...
30, 'r', 'filled');
title('网络拓扑');
xlabel('X坐标');
ylabel('Y坐标');
legend('基站', '用户', 'Location', 'best');
grid on;
axis equal;
% 子图2: 干扰矩阵
subplot(2, 3, 2);
imagesc(ga.problem.interference_map);
colorbar;
title('干扰矩阵');
xlabel('小区编号');
ylabel('小区编号');
% 子图3: 需求矩阵
subplot(2, 3, 3);
imagesc(ga.problem.demand_matrix);
colorbar;
title('信道需求矩阵');
xlabel('信道编号');
ylabel('小区编号');
% 子图4: 最优分配方案
subplot(2, 3, 4);
imagesc(ga.best_solution);
colorbar;
title('最优信道分配');
xlabel('信道编号');
ylabel('小区编号');
% 子图5: 收敛曲线
subplot(2, 3, 5);
plot(1:ga.max_generations, ga.convergence, 'b-', 'LineWidth', 2);
title('收敛曲线');
xlabel('进化代数');
ylabel('最优适应度');
grid on;
% 子图6: 性能指标
subplot(2, 3, 6);
metrics = calculate_performance_metrics(ga);
bar(1:3, [metrics.satisfaction_rate, metrics.interference_level, metrics.utilization]);
set(gca, 'XTickLabel', {'满足率', '干扰水平', '利用率'});
title('性能指标');
ylim([0, 1]);
grid on;
sgtitle('遗传算法动态信道分配结果');
end
function metrics = calculate_performance_metrics(ga)
% 计算性能指标
total_demand = sum(ga.problem.demand_matrix(:));
total_allocated = sum(ga.best_solution(:));
% 计算实际满足的需求
satisfied_demand = 0;
for i = 1:ga.problem.num_cells
for ch = 1:ga.problem.num_channels
satisfied_demand = satisfied_demand + ...
min(ga.best_solution(i, ch), ga.problem.demand_matrix(i, ch));
end
end
metrics.satisfaction_rate = satisfied_demand / total_demand;
metrics.utilization = total_allocated / (ga.problem.num_cells * ga.problem.num_channels * 5);
metrics.interference_level = 1 - ga.best_fitness;
end
end
end
3. 生物免疫算法实现
3.1 生物免疫算法核心类
classdef ImmuneAlgorithm
% 生物免疫算法实现动态信道分配
properties
population_size % 抗体种群大小
memory_size % 记忆细胞数量
max_generations % 最大迭代次数
clone_rate % 克隆率
mutation_rate % 变异率
suppression_threshold % 抑制阈值
problem % 信道分配问题实例
population % 抗体种群
memory_cells % 记忆细胞
fitness % 适应度
affinity % 亲和度
best_solution % 最优解
best_fitness % 最优适应度
convergence % 收敛曲线
end
methods
function ia = ImmuneAlgorithm(problem, config)
% 初始化免疫算法
ia.problem = problem;
ia.population_size = config.population_size;
ia.memory_size = config.memory_size;
ia.max_generations = config.max_generations;
ia.clone_rate = config.clone_rate;
ia.mutation_rate = config.mutation_rate;
ia.suppression_threshold = config.suppression_threshold;
ia.convergence = zeros(config.max_generations, 1);
end
function ia = initialize_population(ia)
% 初始化抗体种群
fprintf('初始化免疫算法种群...\n');
num_cells = ia.problem.num_cells;
num_channels = ia.problem.num_channels;
ia.population = zeros(ia.population_size, num_cells, num_channels);
ia.fitness = zeros(ia.population_size, 1);
ia.affinity = zeros(ia.population_size, 1);
for i = 1:ia.population_size
solution = generate_random_solution(ia, num_cells, num_channels);
ia.population(i, :, :) = solution;
ia.fitness(i) = ia.problem.evaluate_allocation(solution);
end
% 初始化记忆细胞
ia.memory_cells = zeros(ia.memory_size, num_cells, num_channels);
ia = update_memory_cells(ia);
% 记录最优解
[ia.best_fitness, best_idx] = max(ia.fitness);
ia.best_solution = squeeze(ia.population(best_idx, :, :));
end
function solution = generate_random_solution(ia, num_cells, num_channels)
% 生成随机解
solution = zeros(num_cells, num_channels);
for cell = 1:num_cells
for ch = 1:num_channels
if rand() < 0.4
max_users = ia.problem.demand_matrix(cell, ch);
if max_users > 0
solution(cell, ch) = randi([0, max_users]);
end
end
end
end
end
function ia = evolve(ia)
% 执行免疫算法进化
fprintf('开始免疫算法进化...\n');
for gen = 1:ia.max_generations
% 计算亲和度
ia = calculate_affinity(ia);
% 克隆选择
ia = clonal_selection(ia);
% 亲和度成熟（变异）
ia = affinity_maturation(ia);
% 网络抑制
ia = network_suppression(ia);
% 更新记忆细胞
ia = update_memory_cells(ia);
% 更新最优解
[current_best_fitness, best_idx] = max(ia.fitness);
if current_best_fitness > ia.best_fitness
ia.best_fitness = current_best_fitness;
ia.best_solution = squeeze(ia.population(best_idx, :, :));
end
ia.convergence(gen) = ia.best_fitness;
if mod(gen, 10) == 0
fprintf('代数 %d: 最优适应度 = %.4f\n', gen, ia.best_fitness);
end
end
end
function ia = calculate_affinity(ia)
% 计算抗体亲和度（基于适应度和多样性）
ia.affinity = ia.fitness; % 基础亲和度 = 适应度
% 添加多样性惩罚
for i = 1:ia.population_size
diversity_penalty = 0;
for j = 1:ia.population_size
if i ~= j
% 计算抗体间相似度
similarity = calculate_similarity(...
squeeze(ia.population(i, :, :)), ...
squeeze(ia.population(j, :, :)));
diversity_penalty = diversity_penalty + similarity;
end
end
% 相似度越高，亲和度惩罚越大
ia.affinity(i) = ia.affinity(i) - 0.1 * (diversity_penalty / (ia.population_size-1));
end
% 确保亲和度非负
ia.affinity = max(0, ia.affinity);
end
function similarity = calculate_similarity(antibody1, antibody2)
% 计算两个抗体的相似度
diff = abs(antibody1 - antibody2);
max_possible_diff = max(antibody1(:)) + max(antibody2(:));
if max_possible_diff == 0
similarity = 0;
else
similarity = sum(diff(:)) / (numel(antibody1) * max_possible_diff);
end
end
function ia = clonal_selection(ia)
% 克隆选择操作
num_cells = ia.problem.num_cells;
num_channels = ia.problem.num_channels;
% 根据亲和度选择抗体进行克隆
clone_population = zeros(ia.population_size, num_cells, num_channels);
clone_fitness = zeros(ia.population_size, 1);
clone_affinity = zeros(ia.population_size, 1);
clone_count = 1;
for i = 1:ia.population_size
clone_num = round(ia.affinity(i) * ia.clone_rate * ia.population_size);
clone_num = max(1, clone_num); % 至少克隆1个
for j = 1:clone_num
if clone_count > ia.population_size
break;
end
clone_population(clone_count, :, :) = ia.population(i, :, :);
clone_fitness(clone_count) = ia.fitness(i);
clone_affinity(clone_count) = ia.affinity(i);
clone_count = clone_count + 1;
end
if clone_count > ia.population_size
break;
end
end
% 填充剩余位置
while clone_count <= ia.population_size
idx = randi(ia.population_size);
clone_population(clone_count, :, :) = ia.population(idx, :, :);
clone_fitness(clone_count) = ia.fitness(idx);
clone_affinity(clone_count) = ia.affinity(idx);
clone_count = clone_count + 1;
end
ia.population = clone_population;
ia.fitness = clone_fitness;
ia.affinity = clone_affinity;
end
function ia = affinity_maturation(ia)
% 亲和度成熟（变异操作）
num_cells = ia.problem.num_cells;
num_channels = ia.problem.num_channels;
for i = 1:ia.population_size
individual = squeeze(ia.population(i, :, :));
% 变异概率与亲和度成反比
mutation_prob = ia.mutation_rate * (1 - ia.affinity(i));
for cell = 1:num_cells
for ch = 1:num_channels
if rand() < mutation_prob
max_users = ia.problem.demand_matrix(cell, ch);
if max_users > 0
% 基于当前值的智能变异
current_val = individual(cell, ch);
if rand() < 0.5 && current_val < max_users
individual(cell, ch) = current_val + 1;
elseif current_val > 0
individual(cell, ch) = current_val - 1;
else
individual(cell, ch) = randi([0, max_users]);
end
else
individual(cell, ch) = 0;
end
end
end
end
ia.population(i, :, :) = individual;
ia.fitness(i) = ia.problem.evaluate_allocation(individual);
end
end
function ia = network_suppression(ia)
% 网络抑制（去除相似抗体）
num_cells = ia.problem.num_cells;
num_channels = ia.problem.num_channels;
to_remove = false(ia.population_size, 1);
for i = 1:ia.population_size
if to_remove(i), continue; end
for j = i+1:ia.population_size
if ~to_remove(j)
similarity = calculate_similarity(...
squeeze(ia.population(i, :, :)), ...
squeeze(ia.population(j, :, :)));
if similarity > ia.suppression_threshold
% 移除适应度较低的抗体
if ia.fitness(i) > ia.fitness(j)
to_remove(j) = true;
else
to_remove(i) = true;
break;
end
end
end
end
end
% 移除标记的抗体并用新抗体替换
remove_count = sum(to_remove);
if remove_count > 0
new_antibodies = zeros(remove_count, num_cells, num_channels);
new_fitness = zeros(remove_count, 1);
for k = 1:remove_count
new_solution = generate_random_solution(ia, num_cells, num_channels);
new_antibodies(k, :, :) = new_solution;
new_fitness(k) = ia.problem.evaluate_allocation(new_solution);
end
ia.population(to_remove, :, :) = new_antibodies;
ia.fitness(to_remove) = new_fitness;
end
end
function ia = update_memory_cells(ia)
% 更新记忆细胞
[sorted_fitness, sorted_indices] = sort(ia.fitness, 'descend');
top_indices = sorted_indices(1:min(ia.memory_size, ia.population_size));
for i = 1:length(top_indices)
ia.memory_cells(i, :, :) = ia.population(top_indices(i), :, :);
end
% 如果记忆细胞未满，用随机解填充
if length(top_indices) < ia.memory_size
for i = length(top_indices)+1:ia.memory_size
ia.memory_cells(i, :, :) = generate_random_solution(...
ia, ia.problem.num_cells, ia.problem.num_channels);
end
end
end
function plot_convergence(ia)
% 绘制收敛曲线
figure;
plot(1:ia.max_generations, ia.convergence, 'r-', 'LineWidth', 2);
xlabel('进化代数');
ylabel('最优适应度');
title('免疫算法收敛曲线');
grid on;
end
end
end
4. 主程序与比较分析
4.1 主程序实现
function main_dynamic_channel_allocation()
% 动态信道分配主程序
fprintf('=== 动态信道分配算法比较 ===\n\n');
% 系统配置
config.num_cells = 7; % 7个小区
config.num_channels = 10; % 10个信道
config.num_users = 50; % 50个用户
% 遗传算法参数
ga_config.population_size = 50;
ga_config.max_generations = 100;
ga_config.crossover_rate = 0.8;
ga_config.mutation_rate = 0.1;
ga_config.elitism_count = 5;
% 免疫算法参数
ia_config.population_size = 50;
ia_config.memory_size = 10;
ia_config.max_generations = 100;
ia_config.clone_rate = 0.3;
ia_config.mutation_rate = 0.15;
ia_config.suppression_threshold = 0.1;
% 创建问题实例
fprintf('初始化动态信道分配问题...\n');
problem = DynamicChannelAllocation(config);
% 运行遗传算法
fprintf('\n--- 遗传算法 ---\n');
tic;
ga = GeneticAlgorithm(problem, ga_config);
ga = initialize_population(ga);
ga = evolve(ga);
ga_time = toc;
fprintf('遗传算法运行时间: %.2f 秒\n', ga_time);
fprintf('遗传算法最优适应度: %.4f\n', ga.best_fitness);
% 运行免疫算法
fprintf('\n--- 免疫算法 ---\n');
tic;
ia = ImmuneAlgorithm(problem, ia_config);
ia = initialize_population(ia);
ia = evolve(ia);
ia_time = toc;
fprintf('免疫算法运行时间: %.2f 秒\n', ia_time);
fprintf('免疫算法最优适应度: %.4f\n', ia.best_fitness);
% 性能比较
fprintf('\n--- 性能比较 ---\n');
compare_algorithms(ga, ia, problem);
% 可视化结果
fprintf('\n生成可视化结果...\n');
visualize_comparison(ga, ia, problem);
end
function compare_algorithms(ga, ia, problem)
% 比较两种算法的性能
% 计算性能指标
ga_metrics = calculate_detailed_metrics(problem, ga.best_solution);
ia_metrics = calculate_detailed_metrics(problem, ia.best_solution);
fprintf('\n性能指标对比:\n');
fprintf('%-15s %-10s %-10s %-10s\n', '指标', '遗传算法', '免疫算法', '改善');
fprintf('%-15s %-10.4f %-10.4f %-10.4f\n', '需求满足率', ...
ga_metrics.satisfaction_rate, ia_metrics.satisfaction_rate, ...
ia_metrics.satisfaction_rate - ga_metrics.satisfaction_rate);
fprintf('%-15s %-10.4f %-10.4f %-10.4f\n', '信道利用率', ...
ga_metrics.utilization, ia_metrics.utilization, ...
ia_metrics.utilization - ga_metrics.utilization);
fprintf('%-15s %-10.4f %-10.4f %-10.4f\n', '干扰水平', ...
ga_metrics.interference, ia_metrics.interference, ...
ia_metrics.interference - ga_metrics.interference);
fprintf('%-15s %-10.4f %-10.4f %-10.4f\n', '公平性指数', ...
ga_metrics.fairness, ia_metrics.fairness, ...
ia_metrics.fairness - ga_metrics.fairness);
end
function metrics = calculate_detailed_metrics(problem, solution)
% 计算详细的性能指标
total_demand = sum(problem.demand_matrix(:));
total_allocated = sum(solution(:));
max_possible = problem.num_cells * problem.num_channels * 5;
% 计算需求满足情况
satisfied_demand = 0;
for i = 1:problem.num_cells
for ch = 1:problem.num_channels
satisfied_demand = satisfied_demand + ...
min(solution(i, ch), problem.demand_matrix(i, ch));
end
end
% 计算干扰
interference = 0;
interference_pairs = 0;
for ch = 1:problem.num_channels
for i = 1:problem.num_cells
if solution(i, ch) > 0
for j = i+1:problem.num_cells
if solution(j, ch) > 0
interference = interference + ...
problem.interference_map(i, j) * solution(i, ch) * solution(j, ch);
interference_pairs = interference_pairs + 1;
end
end
end
end
end
% 计算公平性（Jain's Fairness Index）
cell_utilization = zeros(problem.num_cells, 1);
for i = 1:problem.num_cells
cell_utilization(i) = sum(solution(i, :)) / sum(problem.demand_matrix(i, :));
end
cell_utilization(isnan(cell_utilization)) = 0;
fairness = (sum(cell_utilization))^2 / (problem.num_cells * sum(cell_utilization.^2));
if isnan(fairness), fairness = 0; end
metrics.satisfaction_rate = satisfied_demand / total_demand;
metrics.utilization = total_allocated / max_possible;
metrics.interference = interference / (problem.num_cells^2 * max(solution(:))^2);
metrics.fairness = fairness;
end
function visualize_comparison(ga, ia, problem)
% 可视化比较结果
figure('Position', [50, 50, 1400, 900]);
% 收敛曲线比较
subplot(2, 3, 1);
plot(1:ga.max_generations, ga.convergence, 'b-', 'LineWidth', 2);
hold on;
plot(1:ia.max_generations, ia.convergence, 'r-', 'LineWidth', 2);
xlabel('进化代数');
ylabel('最优适应度');
title('算法收敛曲线比较');
legend('遗传算法', '免疫算法', 'Location', 'southeast');
grid on;
% 性能指标比较
subplot(2, 3, 2);
ga_metrics = calculate_detailed_metrics(problem, ga.best_solution);
ia_metrics = calculate_detailed_metrics(problem, ia.best_solution);
metrics_names = {'需求满足率', '信道利用率', '干扰水平', '公平性'};
ga_values = [ga_metrics.satisfaction_rate, ga_metrics.utilization, ...
1 - ga_metrics.interference, ga_metrics.fairness];
ia_values = [ia_metrics.satisfaction_rate, ia_metrics.utilization, ...
1 - ia_metrics.interference, ia_metrics.fairness];
bar_data = [ga_values; ia_values]';
bar(bar_data);
set(gca, 'XTickLabel', metrics_names);
ylabel('性能值');
title('性能指标比较');
legend('遗传算法', '免疫算法', 'Location', 'best');
grid on;
% 遗传算法分配方案
subplot(2, 3, 3);
imagesc(ga.best_solution);
colorbar;
title('遗传算法分配方案');
xlabel('信道编号');
ylabel('小区编号');
% 免疫算法分配方案
subplot(2, 3, 4);
imagesc(ia.best_solution);
colorbar;
title('免疫算法分配方案');
xlabel('信道编号');
ylabel('小区编号');
% 需求矩阵
subplot(2, 3, 5);
imagesc(problem.demand_matrix);
colorbar;
title('信道需求矩阵');
xlabel('信道编号');
ylabel('小区编号');
% 干扰矩阵
subplot(2, 3, 6);
imagesc(problem.interference_map);
colorbar;
title('小区干扰矩阵');
xlabel('小区编号');
ylabel('小区编号');
sgtitle('动态信道分配算法比较结果');
end
% 运行主程序
main_dynamic_channel_allocation();
5. 实时动态调整
5.1 动态环境模拟
classdef DynamicEnvironment
% 动态环境模拟
properties
base_problem % 基础问题
time_steps % 时间步数
traffic_patterns % 流量模式
mobility_model % 移动性模型
event_schedule % 事件调度
end
methods
function de = DynamicEnvironment(base_problem, time_steps)
de.base_problem = base_problem;
de.time_steps = time_steps;
de = initialize_traffic_patterns(de);
de = initialize_mobility_model(de);
end
function de = initialize_traffic_patterns(de)
% 初始化流量模式（日间变化）
de.traffic_patterns = zeros(de.time_steps, de.base_problem.num_cells);
for t = 1:de.time_steps
% 模拟日间流量变化
hour = mod(t, 24);
if hour >= 8 && hour <= 18
% 白天高峰期
base_load = 0.7 + 0.3 * sin(2*pi*(hour-8)/10);
else
% 夜间低峰期
base_load = 0.3 + 0.2 * cos(2*pi*(hour)/24);
end
% 添加随机波动
for cell = 1:de.base_problem.num_cells
de.traffic_patterns(t, cell) = base_load * (0.8 + 0.4*rand());
end
end
end
function problem = get_problem_at_time(de, t)
% 获取特定时间的信道分配问题
problem = de.base_problem;
% 根据流量模式调整需求矩阵
scale_factors = de.traffic_patterns(t, :);
for cell = 1:problem.num_cells
problem.demand_matrix(cell, :) = ...
problem.demand_matrix(cell, :) * scale_factors(cell);
end
end
function run_dynamic_simulation(de)
% 运行动态仿真
fprintf('开始动态信道分配仿真...\n');
% 初始化算法
config.num_cells = de.base_problem.num_cells;
config.num_channels = de.base_problem.num_channels;
config.num_users = de.base_problem.num_users;
ga_config.population_size = 30;
ga_config.max_generations = 50;
ia_config.population_size = 30;
ia_config.max_generations = 50;
% 存储历史性能
ga_performance = zeros(de.time_steps, 1);
ia_performance = zeros(de.time_steps, 1);
for t = 1:de.time_steps
fprintf('时间步 %d/%d\n', t, de.time_steps);
% 获取当前时间的问题
current_problem = get_problem_at_time(de, t);
% 遗传算法
ga = GeneticAlgorithm(current_problem, ga_config);
ga = initialize_population(ga);
ga = evolve(ga);
ga_performance(t) = ga.best_fitness;
% 免疫算法
ia = ImmuneAlgorithm(current_problem, ia_config);
ia = initialize_population(ia);
ia = evolve(ia);
ia_performance(t) = ia.best_fitness;
end
% 绘制动态性能
figure;
plot(1:de.time_steps, ga_performance, 'b-', 'LineWidth', 2);
hold on;
plot(1:de.time_steps, ia_performance, 'r-', 'LineWidth', 2);
plot(1:de.time_steps, de.traffic_patterns(:, 1), 'g--', 'LineWidth', 1);
xlabel('时间步');
ylabel('性能指标');
title('动态环境下的算法性能');
legend('遗传算法', '免疫算法', '流量负载', 'Location', 'best');
grid on;
end
end
end
参考代码 用生物免疫算法和遗传算法实现动态信道分配 www.youwenfan.com/contentcnl/80038.html
总结
这个完整的动态信道分配解决方案提供了：
主要特点：
- 完整的问题建模：包含网络拓扑、干扰模型、需求分析
- 双算法实现：遗传算法和生物免疫算法
- 动态环境适应：支持时变流量模式
- 全面性能评估：多维度性能指标比较
- 可视化分析：丰富的图形化结果展示
算法优势：
- 遗传算法：全局搜索能力强，实现相对简单
- 免疫算法：具有记忆功能和多样性保持，适应动态环境更好
应用价值：
- 为5G/6G网络资源管理提供智能解决方案
- 可扩展至大规模网络场景
- 支持实时动态调整和优化
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