用matlab写一段基于遗传算法的考虑网络拓扑效率的韧性修复策略代码

由于题目描述不够具体，下面提供一种基于遗传算法的考虑网络拓扑效率的韧性修复策略代码实现。

假设有一个网络拓扑结构的邻接矩阵A，其中A(i,j)=1表示节点i和节点j之间有一条连边，A(i,j)=0表示没有连边。在网络结构发生损坏时，我们需要通过调整拓扑结构来提高网络韧性。

具体来说，我们可以定义一个代表拓扑结构的染色体，其中每个基因表示一个节点的度数，即与该节点相连的边数。在修复网络时，我们通过遗传算法优化染色体，使得网络拓扑结构更为健壮。

以下是基于遗传算法的考虑网络拓扑效率的韧性修复策略代码实现：

% 遗传算法参数设置
popsize = 100; % 种群大小
maxgen = 50; % 最大迭代次数
pc = 0.8; % 交叉概率
pm = 0.1; % 变异概率

% 初始化种群
pop = round(rand(popsize, size(A,1)));

% 遗传算法迭代
for gen = 1:maxgen
% 计算适应度
fit = sum(pop, 2);

% 选择操作
[fit_sort, fit_index] = sort(fit, 'descend');
pop_sort = pop(fit_index,:);
pop_new = pop_sort(1:popsize/2,:);

% 交叉操作
pop_cross = zeros(popsize/2, size(A,1));
for i = 1:popsize/2
if rand < pc
index1 = randi([1 popsize/2]);
index2 = randi([1 popsize/2]);
while index1 == index2
index2 = randi([1 popsize/2]);
end
parent1 = pop_new(index1,:);
parent2 = pop_new(index2,:);
cross_index = randi([2 size(A,1)-1]);
child1 = [parent1(1:cross_index) parent2(cross_index+1:end)];
child2 = [parent2(1:cross_index) parent1(cross_index+1:end)];
pop_cross(i,:) = child1;
pop_cross(i+1,:) = child2;
else
pop_cross(i,:) = pop_new(i,:);
pop_cross(i+1,:) = pop_new(i+1,:);
end
end

% 变异操作
pop_mutate = zeros(popsize/2, size(A,1));
for i = 1:popsize/2
if rand < pm
index = randi([1 popsize/2]);
parent = pop_new(index,:);
mutate_index = randi([1 size(A,1)]);
parent(mutate_index) = 1 - parent(mutate_index);
pop_mutate(i,:) = parent;
else
pop_mutate(i,:) = pop_new(i,:);
end
end

% 合并新种群
pop = [pop_new; pop_cross; pop_mutate];

% 修复网络拓扑结构
pop_sort = sortrows(pop, -1:size(A,1)-1);
for i = 1:popsize
% 修复拓扑结构
node_degree = pop_sort(i,:);
node_degree_index = find(node_degree==1);
while ~isempty(node_degree_index)
node_index = node_degree_index(1);
edge_index = find(A(node_index,:)==1);
A(node_index,edge_index) = 0;
A(edge_index,node_index) = 0;
node_degree(edge_index) = node_degree(edge_index)-1;
node_degree(node_index) = 0;
node_degree_index = find(node_degree==1);
end

% 计算网络效率
D = graphallshortestpaths(sparse(A));
D(D==Inf) = 0;
E = sum(sum(D))/(size(A,1)*(size(A,1)-1));
if E > 0.8 % 修复成功
break;
end
end

% 判断是否修复成功
if E > 0.8
break;
end
end

if gen == maxgen+1
disp('未能成功修复网络拓扑结构');
else
disp(['成功修复网络拓扑结构，迭代次数为' num2str(gen)]);
end

参考文献：

1. E. Şahin, C. Ersoy, and H. Deliç, "Resilience of complex networks: A survey of modeling approaches," Physical Communication, vol. 18, pp. 251-282, 2016.

2. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.