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MATLAB代做|FPGA代做|python代做-spea2多目标进化算法对两个目标的多目标优化问题

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  核心提示:MATLAB代做|FPGA代做|python代做-spea2多目标进化算法对两个目标的多目标优化问题...
function SPEA2_Demo() 
% SPEA2_Demo.m 
% SPEA2 performs experiment presented in our paper: 
% Maoguo Gong, Licheng Jiao.  
% Multiobjective Immune Algorithm with Pareto-optimal Neighbor-based Selection. 
% Technical Report No. IIIP-06-05, Institute of Intelligent Information Processing,  
% Xidian University, Xi'an, China. April 2006. 
% 
% Authors: Maoguo Gong and Licheng Jiao 
% April 7, 2006 
% Copyright (C) 2005-2006 by Maoguo Gong (e-mail: gongmg@126.com) 
% 
% SPEA2 was proposed by Eckart Zitzler, Marco Laumanns and Lothar Thiele. 
% SPEA2: Improving the Strength Pareto Evolutionary Algorithm,  
% in K. Giannakoglou, D. Tsahalis, J. Periaux, P. Papailou and T. Fogarty (eds.) 
% EUROGEN 2001, Evolutionary Methods for Design, Optimization and Control  
% with Applications to Industrial Problems, Athens, Greece, 2002: 95-100.  
% SPEA2 is Copyrighted by the Authors. 
%-------------------------------------------------------------------------- 
clear all; 
%-------------------------------------------------------------------------- 
disp(sprintf('SPEA2 min')); 
disp(sprintf('Authour: Maoguo Gong and Licheng Jiao')); 
EMOinstruction;%% display the instruction for running the programming. 
%-------------------------------------------------------------------------- 
TestNO=input('press the enter key after inputting the serial number of test problem:'); 
Trial=input('input the number of independent runs:'); 
N=100;%population size 
Ne=100;%archive size 
gmax=5;%the maximum number of iterations 
[bu,bd,testfunction]=getbud(TestNO);%bu denotes the upper boundary of variable;bd denotes the nether boundary of variable  
c=size(bu,2);%decision vector dimension 
pc=1;%Cross probability 
pm=1/c;%mutation probability 
if bu==bd return;end 
%-------------------------------------------------------------------------- 
Datime=date; 
Datime(size(Datime,2)-4:size(Datime,2))=[];%%test date 
TestTime=clock;%%test time 
TestTime=[num2str(TestTime(4)),'-',num2str(TestTime(5))]; 
Method='SPEAII'; 
paretof=[];sizepa=[]; 
%-------------------------------------------------------------------------- 
 
runtime=[]; 
for trial=1:Trial 
    timerbegin=clock; 
%-------------------------------------------------------------------------- 
NPOP=rand(N,c).*(ones(N,1)*bu-ones(N,1)*bd)+ones(N,1)*bd; 
EPOP=[];Epa=[];%Initialization 
%-------------------------------------------------------------------------- 
it=0; 
FASPEA2fti=0;APESPEA2ti=0;BTSti=0;SBXti=0;PNmti=0; 
while it<gmax 
%-------------------------------------------------------------------------- 
twoPOP=[NPOP;EPOP]; 
Npa=OVcom(NPOP,TestNO);%compute objective values 
twopa=[Npa;Epa]; 
[Fit,FASPEA2t]=FASPEA2f(twopa);%fitness assignment 
%-------------------------------------------------------------------------- 
[EPOP,Epa,APESPEA2t]=APESPEA2f(twoPOP,twopa,Fit,Ne);%enviromental selection 
%-------------------------------------------------------------------------- 
[EFit,FASPEA2t2]=FASPEA2f(Epa); 
[NPOP,BTSt]=BTSf(EPOP,EFit,N);%selection 
[NPOP,SBXt]=SBXcross(NPOP,bu,bd,pc);%crossover 
[NPOP,PNmt]=PNmutation(NPOP,bu,bd,pm);%mutation 
%-------------------------------------------------------------------------- 
it=it+1; 
disp(sprintf('time: %d   generation: %d ',trial,it)); 
end  %the end of iterations 
%-------------------------------------------------------------------------- 
twoPOP=[NPOP;EPOP]; 
Npa=OVcom(NPOP,TestNO);%compute objective values 
twopa=[Npa;Epa]; 
[Fit,FASPEA2t]=FASPEA2f(twopa);%fitness assignment 
%-------------------------------------------------------------------------- 
[EPOP,Epa,APESPEA2t]=APESPEA2f(twoPOP,twopa,Fit,Ne);%enviromental selection 
[NS(trial),NF]=size(Epa);Trials=trial; 
paretof=[paretof;Epa]; 
runtime(trial)=etime(clock,timerbegin); 
end  %the end of runs 
%-------------------------------------------------------------------------- 
% end %the end of TestNO 
Frontshow(Epa); % plot the Pareto fronts solved by the last run
 

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作者:spea2多目标 来源:spea2多目标
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