ABC算法

ABC（Artificial Bee Colony）算法，人工蜂群算法，是由Karaboga于2005年提出的一种新颖的基于群智能的全局优化算法，来源于蜂群的采蜜行为，蜜蜂根据各自的分工进行不同的活动，并实现蜂群信息的共享和交流，从而找到问题的最优解。人工蜂群算法属于群智能优化算法的一种。
该方法主要分为三个阶段：采蜜蜂发现新的蜜源、观察蜂根据采蜜蜂的蜜源寻找新的蜜源，侦查蜂寻找新的蜂源代替多次没有更新的蜜源；
算法流程如下：
1、初始化蜜源及其花蜜量（自变量向量及对应的适应度函数值），最优的蜜源和花蜜量；
2、对于每一个蜜源：利用当前蜜源信息、任一蜜源（随机选择）和最优蜜源寻找新的蜜源，选择更新，同时记录每个蜜源未更新的次数到，同时更新蜂群最优蜜源及花蜜量；
3、对于每一个蜜源：以一定概率更新每个蜜源，这个概率和每个蜜源的花蜜量相关，花蜜量越大的蜜源被更新的可能性越大，同时更新蜂群最优蜜源及花蜜量；；
4、统计每个蜜源更新的次数，最大未更新次数查过预定值，随机初始化蜜源信息；
5、达到终止条件结束，输出最优蜜源和花蜜量；否则转至步骤2；
附一个简单的ABC代码：

function [best_x,best_f,FEs_cut,avgErr_FEs,bestErr_FEs,sussFlag]= ABC(D,Max_FEs,func_num,LB,UB,opt_f,err)%by zxr 2017/3NP = D; %No. of foodsSF = 1;limit = 200;FoodNumber=ceil(NP/2); %/*The number of food sources equals the half of the colony size*/% avgErr_FEs = -1*ones(1,Max_FEs);% bestErr_FEs = -1*ones(1,Max_FEs);% limit=FoodNumber*D;% /*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 */%/* Control Parameters of ABC algorithm*/Range = UB-LB;Lower = LB;% ObjVal=benchmark_func(Foods, func_num);for FoodID = 1:FoodNumber    Foods(FoodID,:) = rand(1,D) .* Range + Lower;    ObjVal(FoodID) =  func(Foods(FoodID,:), func_num, opt_f);endFEs = FoodNumber;Fitness=calculateFitness(ObjVal);%reset trial counterstrial=zeros(1,FoodNumber);%/*The best food source is memorized*/BestInd=find(ObjVal==min(ObjVal));BestInd=BestInd(end);GlobalMin=ObjVal(BestInd);GlobalParams=Foods(BestInd,:);avgErr_FEs(1:FEs)= mean(ObjVal);bestErr_FEs(1:FEs)= GlobalMin ;stop = 0;FEs_cut = -1;sussFlag = 0;while (stop < 1 ),%%%%%%%%% EMPLOYED BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%    for i=1:(FoodNumber)            %/*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,:);          for j=1:D                   sol(j)=Foods(i,j)+(Foods(i,j)-Foods(neighbour,j))*(rand-0.5)*2 * SF;          end        %  /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/        ind=find(sol<LB);        sol(ind)=LB(ind);        ind=find(sol>UB);        sol(ind)=UB(ind);        %evaluate new solution        ObjValSol=func(sol, func_num,opt_f);         FEs = FEs + 1;         if (FEs>Max_FEs) % Too many FE            stop=1;            break;         end         if GlobalMin <= err && ~sussFlag            sussFlag = 1;            FEs_cut = FEs;        end        FitnessSol=calculateFitness(ObjValSol);       % /*a greedy selection is applied between the current solution i and its mutant*/       if (FitnessSol>Fitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/            Foods(i,:)=sol;            Fitness(i)=FitnessSol;            ObjVal(i)=ObjValSol;            trial(i)=0;        else            trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/       end;        avgErr_FEs(FEs)= mean(ObjVal) ;        bestErr_FEs(FEs)= GlobalMin ;      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;%%%%%%%%%%%%%%%%%%%%%%%% ONLOOKER BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%i=1;t=0;while(t<FoodNumber)    if(rand<prob(i))        t=t+1;            %/*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,:);          for j=1:D                   sol(j)=Foods(i,j)+(Foods(i,j)-Foods(neighbour,j))*(rand-0.5)*2 * SF;          end        %  /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/        ind=find(sol<LB);        sol(ind)=LB(ind);        ind=find(sol>UB);        sol(ind)=UB(ind);        %evaluate new solution        ObjValSol=func(sol, func_num,opt_f);         FEs = FEs + 1;         if (FEs>Max_FEs) % Too many FE            stop=1;            break;         end           if GlobalMin <= err && ~sussFlag            sussFlag = 1;            FEs_cut = FEs;        end        FitnessSol=calculateFitness(ObjValSol);       % /*a greedy selection is applied between the current solution i and its mutant*/       if (FitnessSol>Fitness(i)) %/*If the mutant solution is better than the current solution i, replace the solution with the mutant and reset the trial counter of solution i*/            Foods(i,:)=sol;            Fitness(i)=FitnessSol;            ObjVal(i)=ObjValSol;            trial(i)=0;        else            trial(i)=trial(i)+1; %/*if the solution i can not be improved, increase its trial counter*/       end;        avgErr_FEs(FEs)= mean(ObjVal) ;        bestErr_FEs(FEs)= GlobalMin ;     end;    i=i+1;    if (i==(FoodNumber)+1)         i=1;    end;   end; %/*The best food source is memorized*/     ind=find(ObjVal==min(ObjVal));     ind=ind(end);     if (ObjVal(ind)<GlobalMin)     GlobalMin=ObjVal(ind);     GlobalParams=Foods(ind,:);     end;%%%%%%%%%%%% SCOUT BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%/*determine the food sources whose trial counter exceeds the "limit" value. %In Basic ABC, only one scout is allowed to occur in each cycle*/ind=find(trial==max(trial));ind=ind(end);if (trial(ind)>limit)    trial(ind)=0;%Bas(ind)=0;    sol=(UB-LB).*rand(1,D)+LB;    ObjValSol=func(sol, func_num,opt_f);     FEs = FEs + 1;    if (FEs>Max_FEs) % Too many FE        stop=1;        break;     end      if GlobalMin <= err && ~sussFlag        sussFlag = 1;        FEs_cut = FEs;    end    FitnessSol=calculateFitness(ObjValSol);    Foods(ind,:)=sol;    Fitness(ind)=FitnessSol;    ObjVal(ind)=ObjValSol;    avgErr_FEs(FEs)= mean(ObjVal) ;    bestErr_FEs(FEs)= GlobalMin ; end;if ( (FEs>=Max_FEs ) )%||((GlobalMin-opt_f) <=err)  stop=1;  endend % End of ABCbest_x =GlobalParams;best_f = GlobalMin;