基于matlab金鹰算法（MOGEO）的多目标优化求解

ubeautqq

一、源代码
0 k( x" J. {- @3 B0 q
- i4 H. V1 A) s- Z

• %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
• %
• %  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);
• 
  

               


* c8 Q1 f3 J& ?二、运行结果
( K# m- b) n- x' J( S
/ Y2 d! S' Z9 ?1 L* q

ExxNEN

基于matlab金鹰算法（MOGEO）的多目标优化求解