企业官网建设流程全解析

1 简介

2 部分代码

%___________________________________________________________________% % Grey Wold Optimizer (GWO) source codes version 1.0 % % You can simply define your cost in a seperate file and load its handle to fobj % The initial parameters that you need are: %__________________________________________ % fobj = @YourCostFunction % dim = number of your variables % Max_iteration = maximum number of generations % SearchAgents_no = number of search agents % lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n % ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n % If all the variables have equal lower bound you can just % define lb and ub as two single number numbers % To run GWO: [Best_score,Best_pos,GWO_cg_curve]=GWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj) %__________________________________________ clear all clc for t = 1:1 SearchAgents_no=30; % Number of search agents Max_iteration=100; % Maximum numbef of iterations % Load details of the selected benchmark function % [lb,ub,dim,fobj]=Get_Functions_details(Function_name); fobj = @heavyoilevaluate; ub = [10,10,10,1500,4000,4500,5,5]; lb = [0,0,0,800,1500,1500,0,0]; dim = size(ub,2); [Best_score,Best_pos,GWO_cg_curve]=GWO(SearchAgents_no,Max_iteration,ub,lb,dim,fobj); display(['The best solution obtained by GWO is : ', num2str(Best_pos)]); display(['Best value by GWO is : ', num2str(Best_score)]); end opt=heavyoilevaluate(Best_pos,20);

3 仿真结果

4 参考文献

[1]陈霄. 基于混沌DNA遗传算法的重油热解过程建模方法:, CN108345933A[P]. 2018.