基于matlab的改进型人工蜂群算法

%-------------------------------------------------------------------------- % Modified ABC-PTS Algorithm in Matlab % Reference: % 1) D. Karaboga, B. Basturk, On The Performance Of Artificial Bee Colony (ABC) % Algorithm, Applied Soft Computing,Volume 8, Issue 1, January 2008, Pages 687-697 % 2) Y. Wang, W. Chen, and C. Tellambura, A PAPR Reduction Method % Based on Artificial Bee Colony Algorithm for OFDM Signals,IEEE Transactions on Wireless Communications, 2010, pp.2994-2999. % % Author: Leo Zhu , Yulong Guo % South China University of Technology , 510640 Guangzhou, China % Email: scut_ee@163.com %-------------------------------------------------------------------------- clear all;close all;clc Int=1; %/* Control Parameters of ABC algorithm*/ NP=64; FoodNumber=NP/2; %/*The number of food sources equals the half of the colony size*/ maxCycle=32; %/*The number of cycles for foraging {a stopping criteria}*/ D=16; %/*The number of parameters of the problem to be optimized*/ % PTS Blocks %limit=FoodNumber*D; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/ limit=10; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/ K=256; % OFDM Subcarriers L=4*K; Max_Symbols=1e3; % 1e2 is ok for approximation pset = [1 -1]; %-------------------------------------------------------------------------- t_32_32_Verified_Set=zeros(1,Max_Symbols); %-------------------------------------------------------------------------- while(Int==1) % objfun='PAPR_Val_Phase2'; %cost function to be optimized ub=ones(1,D-1)*2; %/*lower bounds of the parameters. */ lb=ones(1,D-1)*1;%/*upper bound of the parameters.*/ PAPR_Orignal = zeros(1,Max_Symbols); %PAPR_Orignal_ = zeros(1,Max_Symbols); PAPR_PTS = zeros(1,Max_Symbols); %PAPR_PTS_ = zeros(1,Max_Symbols); Int=2; end for nSymbol=1:Max_Symbols %-------------------------------------------------------------------------- tic %-------------------------------------------------------------------------- % /*All food sources are initialized */ %/*Variables are initialized in the range [lb,ub]. If each parameter has different range, use arrays lb[j], ub[j] instead of lb and ub */ Range = repmat((ub-lb),[FoodNumber 1]); Lower = repmat(lb, [FoodNumber 1]); Foods = [ones(1,FoodNumber)',(randint(FoodNumber,D-1) .* Range + Lower)]; %Phase_Set Init %-------------------------------------------------------------------------- %QPSK=OFDM_Init; % OFDM Symbol Initialization --> QPSK Modulation %QPSK_Set = [1 -1 1i -1i ]; % QPSK Modulation %-------------------------------------------------------------------------- QAM_Set= [-3+3j, -3+1j, -3-1j, -3-3j,... -1+3j, -1+1j, -1-1j, -1-3j, ... 1+3j, 1+1j, 1-1j, 1-3j,.... 3+3j, 3+1j, 3-1j, 3-3j]; Index = randint(1,K,length(QAM_Set))+1; % Index for QPSK QPSK=QAM_Set(Index); ObjVal_Orignal=PAPR_Init(QPSK); % --------------------------PAPR Calculation for oringinal OFDM Symbol PAPR_Orignal(nSymbol) = ObjVal_Orignal; % PAPR_Orignal(nSymbol) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% IFFT=PTS_Init_IFFT(QPSK,D,K); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ObjVal=PAPR_Val_Phase2(IFFT,Foods,L); % --------------------------PAPR Calculation for PTS based OFDM Symbol Fitness=calculateFitness(ObjVal); %reset trial counters trial=zeros(1,FoodNumber); %/*The best food source is memorized*/ BestInd=find(ObjVal==min(ObjVal)); BestInd=BestInd(end); GlobalMin=ObjVal(BestInd); GlobalParams=Foods(BestInd,:); %-------------------------------------------------------------------------- Global_Tmp=GlobalParams; % For GBest ABC-PTS %-------------------------------------------------------------------------- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% step = Max_Symbols /100; % Set the parameters of waitbar %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% iter=1; while ((iter <= maxCycle)), %%%%%%%%% EMPLOYED BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%% for i=1:(FoodNumber) %/*The parameter to be changed is determined randomly*/ Param2Change=fix(rand*(D-1))+2; %/*A randomly chosen solution is used in producing a mutant solution of the solution i*/ neighbour=fix(rand*(FoodNumber))+1; %/*Randomly selected solution must be different from the solution i*/ while(neighbour==i) neighbour=fix(rand*(FoodNumber))+1; end; sol=Foods(i,:); % /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */ %sol(Param2Change)=fix(Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2); %fix to integer %sol(Param2Change)=fix(Foods(i,Param2Change)*(1-rand)+Global_Tmp(Param2Change)*rand); %%{ sol(Param2Change)=(Foods(neighbour,Param2Change)-Foods(i,Param2Change))*rand*(0.7*(maxCycle-iter)/maxCycle+0.3)+.... Global_Tmp(1,Param2Change)*(1-rand)*(0.3*iter/maxCycle+0.7); sol(Param2Change)=fix(sol(Param2Change))+1; %} % /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/ if(sol(Param2Change)<1) sol(Param2Change)=1; end if(sol(Param2Change)>2) sol(Param2Change)=2; end %evaluate new solution ObjValSol=PAPR_Val_Phase2(IFFT,sol,L); % -----------------PAPR Calculation for each solution FitnessSol=calculateFitness(ObjValSol); % /*a greedy selection is applied between the current solution i and its mutant*/ if (FitnessSol>Fitness(i)) Foods(i,:)=sol; %---------------------------------------------------- Foods updated Fitness(i)=FitnessSol; ObjVal(i)=ObjValSol; % -----------------PAPR Comparison for each solution trial(i)=0; else trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/ end; end; %%%%%%%%%%%%%%%%%%%%%%%% CalculateProbabilities %%%%%%%%%%%%%%%%%%%%%%%%%%% %/* A food source is chosen with the probability which is proportioal to its quality*/ %/*Different schemes can be used to calculate the probability values*/ %/*For example prob(i)=fitness(i)/sum(fitness)*/ %/*or in a way used in the metot below prob(i)=a*fitness(i)/max(fitness)+b*/ %/*probability values are calculated by using fitness values and normalized by dividing maximum fitness value*/ %prob=(0.9.*Fitness./max(Fitness))+0.1; prob=Fitness/sum(Fitness); %%%%%%%%%%%%%%%%%%%%%%%% ONLOOKER BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% i=1; t=0; while(t<FoodNumber) %t=t+1; %%%%%%%%%%%%%%%%%%%%%%%%%%%% if(rand<prob(i)) t=t+1; %/*The parameter to be changed is determined randomly*/ Param2Change=fix(rand*(D-1))+2; %/*A randomly chosen solution is used in producing a mutant solution of the solution i*/ neighbour=fix(rand*(FoodNumber))+1; %--------------------------------------------Use Global_Tmp Instead %%{