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 2017/3
NP = D; %No. of foods
SF = 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);
end
FEs = FoodNumber;

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,:);

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(solUB);
        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(tif(rand1;
            %/*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(solUB);
        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)%%%%%%%%%%%% 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;  
end
end % End of ABC

best_x =GlobalParams;
best_f = GlobalMin;