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)genP1 = 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 == 1Cost1(a) = Cost1_intial;       Cost2(a) = Cost2_intial;               elseCost1(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 <=32F2(gen)        = mean(F(1:gen));Cost1gen2(gen) = mean(Cost1gen(1:gen));Cost2gen2(gen) = mean(Cost2gen(1:gen));elseF2(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 onfigure;
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 ondisp('流量');
eas
06_029m