%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  
%  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);
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【多目标优化求解】基于金鹰算法(MOGEO)的多目标优化求解matlab源码_分享