1 简介 2 部分代码 % Adaptive mixed Grey Wold Optimizer % % % % Main paper: % Martin, Benoit, Julien Marot, and Salah Bourennane. % "Mixed grey wolf optimizer % for the joint denoising and unmixing of multispectral images."
1 简介
2 部分代码
% Adaptive mixed Grey Wold Optimizer %% %
% Main paper:
% Martin, Benoit, Julien Marot, and Salah Bourennane.
% "Mixed grey wolf optimizer
% for the joint denoising and unmixing of multispectral images."
% Applied Soft Computing 74 (2019): 385-410.
%--------------------------------------------------------------------
%
% 'amixedGWO'
% 'MixedGWO'
% Main paper:
% Martin, Benoit, Julien Marot, and Salah Bourennane.
% "Mixed grey wolf optimizer
% for the joint denoising and unmixing of multispectral images."
% Applied Soft Computing 74 (2019): 385-410.
%
%
%
%
clear all
close all
clc
NbRuns=1;% 1000
%%% All available optimization methods:
%Optimization_Methods={'amixedGWO','MixedGWO','GWO','GWOCS','GAO','PSO','WOA','ABC','TSA','SA','PSOGSA','MVO','CGSA'};%
%Optimization_Methods={'CGSA'};%,'amixedGWO','PSO','GWO','TSA','CGSA'
%Optimization_Methods={'amixedGWO','PSO','GWO','TSA','CGSA'};%,'amixedGWO','PSO','GWO','TSA','CGSA'
%Optimization_Methods={'WOA','GWO','CGSA'};%,'amixedGWO','PSO','GWO','TSA','CGSA'
%Optimization_Methods={'amixedGWO','PSO'};% 'GCGWO',
Optimization_Methods={'PSO','GWO','amixedGWO'};%,'MVO' % 'MixedGWO','PSOGSA', 'WOA' ,'amixedGWO','ABC'
%Optimization_Methods={'amixedGWO'};
%%% ,'MVO' % 'MixedGWO','PSOGSA', 'WOA' ,'amixedGWO','ABC'
%Optimization_Methods={'GWO'};%,'MVO' % 'MixedGWO','PSOGSA', 'WOA' ,'amixedGWO','ABC'
%Optimization_Methods={'amixedGWO','GWO'};%,'MVO' % 'MixedGWO','PSOGSA', 'WOA' ,'amixedGWO','ABC'
%%% Creation of the parameters which aer valid for all methods
SearchAgents_no=100;%30 % Number of search agents % 40
%%% Too few: mixed GWO does not have time to explore.
%%% if the dimension of the problem increases, you have to increase the
%%% number of search agents.
%%% in dimension 10, choose 200.
%%% in dimension 5, choose 50.
Max_iteration=50; %20
Option_Intense_Graphics=1;
%%% 1: Intense graphics, we display the agents at each iteration.
%%% 0: No display of the agents.
%%% F17 to F23: fixed dimension
%%% It is not recommended to plot them in a 2D space because it does not
%%% represent the actual values of the function, which do not exist in 2D.
Optimizer_Agents_Plot{1}=Option_Intense_Graphics;
Nb_Methods=length(Optimization_Methods);
Option_Log=1;
[Best_score_All_Methods_All_Runs,GMean_Convergence_Curve_All_Methods,AMean_Convergence_Curve_All_Methods]=...
Initialize_Statistic_Results(NbRuns,Nb_Methods,Max_iteration);
% ind=4;
% Function_name=['F',num2str(ind)]; % Name of the test function that can be from F1 to F23
%Function_name='F2';%F1 % NDparabola % Extreme_Slopes % Halong Mountains2D % ProcessImage
%Function_name='F10';%F1 % NDparabola % Extreme_Slopes % Halong Mountains2D % ProcessImage
%Function_name='ExtremeSlopes';
%Function_name='Run_Whole_Classification_in_Optimization';%Classification_Surrogate
%Function_name='Mountains2D';%Mountains2D
%Function_name='Run_Simple_Recognition_in_Optimization';%
%Function_name='Classification_Surrogate';%
Function_name='F10';%
%disp(['Objective function: ',Function_name])
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
%%% Creation of the parameters which are specifically dedicated to adaptive
%%% mixed GWO. These parameters concern the search spaces and should be
%%% adapted depending on the objective function
Create_Parameters_amixed_GWO
for ind_Run=1:NbRuns
%Setup_From_Interface%%% Should be commented if we do not use the interface
Best_score_All_Methods=zeros(1,Nb_Methods);
Best_pos_All_Methods=zeros(dim,Nb_Methods);
Convergence_Curve_All_Methods=zeros(Max_iteration,Nb_Methods);
Computational_Time=zeros(1,Nb_Methods);
for Index_Method=1:Nb_Methods
Optimizer=str2func(Optimization_Methods{1,Index_Method});
disp('%%%%%%%%%%%%%%%%%%%%%%%%')
disp('Optimization algorithm:')
disp(Optimizer)
Optimizer_Agents_Plot{2}=Optimizer;
% disp('We are running: ')
% disp(Optimizer)
t = clock;
[Best_score,Best_pos,Convergence_Curve]=feval(Optimizer,SearchAgents_no,Max_iteration,lb,ub,dim,fobj,Optimizer_Agents_Plot);
% pause
Computational_Time(Index_Method)=etime(clock, t);
Best_score_All_Methods(1,Index_Method)=Best_score;
Best_pos_All_Methods(:,Index_Method)=Best_pos(:);
Convergence_Curve_All_Methods(:,Index_Method)=Convergence_Curve;
GMean_Convergence_Curve_All_Methods(:,Index_Method)=GMean_Convergence_Curve_All_Methods(:,Index_Method).*Convergence_Curve_All_Methods(:,Index_Method);
AMean_Convergence_Curve_All_Methods(:,Index_Method)=AMean_Convergence_Curve_All_Methods(:,Index_Method)+Convergence_Curve_All_Methods(:,Index_Method);
Best_score_All_Methods_All_Runs(ind_Run,Index_Method)=Best_score_All_Methods(1,Index_Method);
end
% Option_Log=0;
Display_Graphic_Results(Function_name,Optimization_Methods,Convergence_Curve_All_Methods,Option_Log,ind_Run)
Display_Numerical_Results(Optimization_Methods,Best_pos_All_Methods,Best_score_All_Methods,Computational_Time)
Save_Results_in_txt_Files(Optimization_Methods,Best_pos_All_Methods,Best_score_All_Methods,Computational_Time,Convergence_Curve_All_Methods)
end
Display_Statistic_Results
3 仿真结果
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4 参考文献
[1] Martin B , Marot J , Bourennane S . Mixed grey wolf optimizer for the joint denoising and unmixing of multispectral images[J]. Applied Soft Computing, 2018, 74.
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