基于GA遗传优化的离散交通网络双层规划模型设计matlab仿真

1.程序功能描述

2.测试软件版本以及运行结果展示

3.核心程序

4.本算法原理

5.完整程序

1.程序功能描述

基于GA遗传优化的离散交通网络双层规划模型设计.优化输出路段1和路段2的收费情况收敛过程。

2.测试软件版本以及运行结果展示

MATLAB2022a版本运行

3.核心程序

.....................................................................
while gen < MAXGEN;   
      rng(gen)
      gen
     
      P1 = 0.9;
      P2 = 1-P1;

      FitnV=ranking(Objv);    
 
      Selch=select('sus',Chrom,FitnV);    
      Selch=recombin('xovsp', Selch,P1);   
      Selch=mut( Selch,P2);   
      phen1=bs2rv(Selch,FieldD);   
      for a=1:1:NIND  
          if  gen == 1
              Cost1(a) = Cost1_intial;       
              Cost2(a) = Cost2_intial;               
          else
              Cost1(a) = phen1(a,1);   
              Cost2(a) = phen1(a,2);   
          end
          
          %计算对应的目标值
          [errs,a1,a2,eas,tas,xa3] = func_obj(Cost1(a),Cost2(a));
          E               = errs;
          JJ(a,1)         = E;
      end 
      Objvsel      =(JJ+eps);    
      [Chrom,Objv] = reins(Chrom,Selch,1,1,Objv,Objvsel);   
      gen          = gen+1; 

      %保存参数收敛过程和误差收敛过程以及函数值拟合结论
      Cost1gen(gen) = mean(Cost1);
      Cost2gen(gen) = mean(Cost2); 
      F(gen)        = mean(JJ);
      if gen <=32
         F2(gen)        = mean(F(1:gen));
         Cost1gen2(gen) = mean(Cost1gen(1:gen));
         Cost2gen2(gen) = mean(Cost2gen(1:gen));
      else
         F2(gen)        = mean(F(gen-32:gen)); 
         Cost1gen2(gen) = mean(Cost1gen(gen-32:gen));
         Cost2gen2(gen) = mean(Cost2gen(gen-32:gen));
      end
end 
 
Cost1f = Cost1gen(end);   
Cost2f = Cost2gen(end);   
 
figure;
plot(F2(2:end),'linewidth',2);
xlabel('迭代次数');
ylabel('上层目标函数');
grid on

figure;
plot(Cost1gen2(2:end),'r','linewidth',2);
hold on
plot(Cost2gen2(2:end),'b','linewidth',2);
xlabel('迭代次数');
ylabel('收费情况');
legend('路段1','路段2');
grid on

disp('流量');
eas
06_029m