%初始染色体个数
chromosome = repmat(my_chromosome,model.NP,1);
%子代染色体
next_chromosome = repmat(my_chromosome,model.NP,1);
​
%种群的适应度值
seeds_fitness=zeros(1,model.NP);
%全局最优
p_global.cost=inf;
%适应度最优值保留
best=zeros(model.MaxIt+1,1);
best(1)=model.globel.cost;
%种群初始化
for i=1:model.NP
    chromosome(i).pos=model.chromosome(i).pos;
    chromosome(i).alpha=model.chromosome(i).alpha;
    chromosome(i).beta=model.chromosome(i).beta;
    chromosome(i).atkalpha=model.chromosome(i).atkalpha;
    chromosome(i).atkbeta=model.chromosome(i).atkbeta;
    chromosome(i).T=model.chromosome(i).T;
    chromosome(i).sol=model.chromosome(i).sol;
    chromosome(i).cost=model.chromosome(i).cost;
    chromosome(i).IsFeasible=model.chromosome(i).IsFeasible;
​
    seeds_fitness(i)=model.seeds_fitness(i);
  for d=1:3
  chromosome(i).vel(d,:)= zeros(1,model.dim);
  end
  %更新历史最优粒子
  chromosome(i).best.pos =chromosome(i).pos;
  chromosome(i).best.alpha =chromosome(i).alpha;
  chromosome(i).best.beta =chromosome(i).beta;
  chromosome(i).best.T =chromosome(i).T;
  chromosome(i).best.sol =chromosome(i).sol;
  chromosome(i).best.cost =chromosome(i).cost;
  %更新全局最优例子
  if p_global.cost > chromosome(i).best.cost
    p_global = chromosome(i).best;
  end
  
end
​
for it=1:model.MaxIt
    %得到最大和平均适应度值
    model.f_max =max(seeds_fitness);
    model.f_avg =mean(seeds_fitness);
   %按照适应度对染色体排序
    sort_array =zeros(model.NP,2);
    for i=1:model.NP
    sort_array(i,:)= [i,chromosome(i).cost];
    end
    %以cost从小到大进行排序
    sort_array =sortrows(sort_array,2);
    model.p_global =p_global;
    %只保留前一半的染色体,后一般抛弃