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
Posted Matlab咨询QQ1575304183
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了多目标优化求解基于金鹰算法(MOGEO)的多目标优化求解matlab源码相关的知识,希望对你有一定的参考价值。
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% 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);
完整代码或仿真咨询QQ1575304183
以上是关于多目标优化求解基于金鹰算法(MOGEO)的多目标优化求解matlab源码的主要内容,如果未能解决你的问题,请参考以下文章
路径规划考虑分配次序的多无人机协同目标分配建模与遗传算法求解