逐步分析给出下列程序的计算公式，并生成算法流程图：function [island_list, total_load] = improved_flood_alg(dg_locations, dg_powers, load_powers, adj_matrix, critical_loads)
% 改进洪水算法 - 增强版（混合种子 + 模拟退火 + 自适应扰动）
island_list = cell(size(dg_locations));
total_load = 0;
for k = 1:length(dg_locations)
dg_node = dg_locations(k);
dg_power = dg_powers(k);
reach = find_reachable_nodes_bfs(dg_node, adj_matrix);
if isempty(reach)
island_list{k} = dg_node;
continue;
end
%—— 1. 多策略混合种子生成 ——
seeds = generate_hybrid_seeds(dg_node, dg_power, load_powers, reach, critical_loads, adj_matrix);
%—— 2. 模拟退火优化 ——
best_nodes = seeds{1}; % 初始解
best_load = sum(load_powers(best_nodes));
% 模拟退火参数
T0 = 100.0; % 初始温度
alpha = 0.92; % 降温系数
nIter = 80; % 增加迭代次数
T = T0;
current_nodes = best_nodes;
current_load = best_load;
for iter = 1:nIter
%—— 自适应扰动规模 ——
if iter <= nIter/3
% 早期：大步长探索
perturbation_size = min(5, length(reach));
elseif iter <= 2*nIter/3
% 中期：中等步长
perturbation_size = min(3, length(reach));
else
% 后期：小步长精细调整
perturbation_size = min(2, length(reach));
end
%—— 生成候选解 ——
cand = adaptive_perturb(current_nodes, reach, load_powers, dg_power, perturbation_size);
load_cand = sum(load_powers(cand));
%—— 模拟退火接受准则 ——
delta_E = load_cand - current_load;
if delta_E > 0 || (rand < exp(delta_E / T) && load_cand <= dg_power)
current_nodes = cand;
current_load = load_cand;
% 更新全局最优
if current_load > best_load
best_nodes = current_nodes;
best_load = current_load;
end
end
% 降温
T = T * alpha;
end
island_list{k} = best_nodes;
total_load = total_load + best_load;
end
end
function seeds = generate_hybrid_seeds(dg_node, dg_power, load_powers, reach, critical_loads, adj_matrix)
% 生成混合种子解
seeds = cell(1, 3);
% 种子1：贪心优先级解
scores = zeros(size(reach));
for i = 1:numel(reach)
n = reach(i);
scores(i) = (
ismember(n, critical_loads) * 25) ...
+ load_powers(n)/25 ...
- abs(n - dg_node)*0.03;
end
[~, idx] = sort(scores, 'descend');
sorted_nodes = reach(idx);
current_nodes = dg_node;
current_load = 0;
for n = sorted_nodes
if current_load + load_powers(n) <= dg_power
current_nodes(end+1) = n; %#ok<AGROW>
current_load = current_load + load_powers(n);
end
end
seeds{1} = current_nodes;
% 种子2：纯负荷容量优先解
[~, idx] = sort(load_powers(reach), 'descend');
sorted_by_load = reach(idx);
current_nodes = dg_node;
current_load = 0;
for n = sorted_by_load
if current_load + load_powers(n) <= dg_power
current_nodes(end+1) = n; %#ok<AGROW>
current_load = current_load + load_powers(n);
end
end
seeds{2} = current_nodes;
% 种子3：DFS启发式解
dfs_nodes = dfs_greedy_seed(dg_node, dg_power, load_powers, reach, adj_matrix);
seeds{3} = dfs_nodes;
% 选择最优种子作为初始解
loads = zeros(1, 3);
for i = 1:3
loads(i) = sum(load_powers(seeds{i}));
end
[~, best_idx] = max(loads);
seeds = {seeds{best_idx}}; % 只返回最优种子
end
function dfs_nodes = dfs_greedy_seed(dg_node, dg_power, load_powers, reach, adj_matrix)
% DFS启发式种子生成
dfs_nodes = dg_node;
current_load = 0;
visited = false(1, length(adj_matrix));
visited(dg_node) = true;
% 从DG节点开始DFS
stack = dg_node;
while ~isempty(stack) && current_load < dg_power
current = stack(end);
stack(end) = [];
% 找到未访问的邻居
neighbors = find(adj_matrix(current, :) & ~visited);
for neighbor = neighbors
if
ismember(neighbor, reach) && ~visited(neighbor)
if current_load + load_powers(neighbor) <= dg_power
dfs_nodes(end+1) = neighbor; %#ok<AGROW>
current_load = current_load + load_powers(neighbor);
visited(neighbor) = true;
stack(end+1) = neighbor; %#ok<AGROW>
end
end
end
end
end
function cand = adaptive_perturb(current_nodes, reach, load_powers, dg_power, perturbation_size)
% 自适应扰动生成候选解
cand = current_nodes;
% 扰动类型选择
if rand < 0.4
% 添加节点
available =
setdiff(reach, cand);
if ~isempty(available)
num_add = min(perturbation_size, length(available));
to_add = available(randperm(length(available), num_add));
cand =
unique([cand, to_add]);
end
elseif rand < 0.7
%
删除节点
if length(cand) > 1
num_remove = min(perturbation_size, length(cand)-1);
to_remove = randperm(length(cand)-1, num_remove) + 1; % 不
删除DG节点
cand(to_remove) = [];
end
else
% 交换节点
if length(cand) > 1 && length(reach) > length(cand)
num_swap = min(perturbation_size, length(cand)-1);
for i = 1:num_swap
if length(cand) > 1
% 随机选择一个非DG节点进行交换
swap_idx = randi(length(cand)-1) + 1;
available =
setdiff(reach, cand);
if ~isempty(available)
cand(swap_idx) = available(randi(length(available)));
end
end
end
end
end
% 确保功率约束
while sum(load_powers(cand)) > dg_power && length(cand) > 1
% 移除负荷最大的非DG节点
non_dg = cand(2:end);
[~, max_idx] = max(load_powers(non_dg));
cand(max_idx + 1) = [];
end
end
本文介绍如何使用Matlab中的unique、ismember及setdiff函数来处理多目标优化算法中的重复个体问题，通过具体实例展示了这些函数的应用。
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