EDA365电子论坛网
标题:
基于matlab金鹰算法（MOGEO）的多目标优化求解
[打印本页]
作者:
ubeautqq
时间:
2021-3-31 15:03
标题:
基于matlab金鹰算法（MOGEO）的多目标优化求解
一、源代码
2 c9 b% o. y8 U; s
y: P/ J$ L# v! w+ J" u; @7 E
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Multi-Objective Golden Eagle Optimizer (MOGEO) source codes version 1.0
%
% Original paper: Abdolkarim Mohammadi-Balani, Mahmoud Dehghan Nayeri,
% Adel Azar, Mohammadreza Taghizadeh-Yazdi,
% Golden Eagle Optimizer: A nature-inspired
% metaheuristic algorithm, Computers & Industrial Engineering.
% To use this code in your own project
% remove the line for 'GetFunctionDetails' function
% and define the following parameters:
% fun : function handle to the .m file containing the objective function
% the .m file you define should accept 'x' as input and return
% a column vector containing objective function values
% nobj : number of objectives
% nvars : number of decision/design variables
% lb : lower bound of decision variables (must be of size 1 x nvars)
% ub : upper bound of decision variables (must be of size 1 x nvars)
%
% MOGEO will return the following:
% x : best solution found
% fval : objective function value of the found solution
%% Inputs
FunctionNumber = 7; % 1-10
options.PopulationSize = 200;
options.ArchiveSize = 100;
options.MaxIterations = 1000;
options.FunctionNumber = FunctionNumber;
%% Run Multi-Objective Golden Eagle Optimizer
[fun,nobj,nvars,lb,ub] = GetFunctionDetails (FunctionNumber);
options.AttackPropensity = [0.5 , 2];
options.CruisePropensity = [1 , 0.5];
[x,fval] = MOGEO (fun,nobj,nvars,lb,ub, options);
. w8 x) ~6 d+ ~ D6 |; o
8 v3 t! x. F2 k$ l
! b5 k! g5 O0 x4 e
9 G; L s0 V! l4 B5 V. x8 _3 ]
二、运行结果
: f% d8 t5 U0 h! S$ P& e b2 s
; q8 w( Q v& R
1.png
(48.03 KB, 下载次数: 9)
下载附件
保存到相册
2021-3-31 15:03 上传
作者:
ExxNEN
时间:
2021-3-31 16:03
基于matlab金鹰算法（MOGEO）的多目标优化求解
欢迎光临 EDA365电子论坛网 (https://bbs.eda365.com/)
Powered by Discuz! X3.2
