ABC算法

ABC算法步骤推荐这个，一步一步讲的很通透， http://mf.erciyes.edu.tr/abc/pub/Step%20by%20Step%20Procedure%20of%20ABC.pdf

跟着用MATLAB 写了下代码。

 %/* ABC algorithm coded using MATLAB language */
 clear all
 close all
 clc
 global objfun D ub lb


 %注：这里没有说明objfun是什么，但是屏现有的知识，可以判断，它对应学习资料《step by step procedure of
 %ABC》上的f(x)= (x_1)^2+(x_2)^2

 %Foods [FoodNumber][D]; /*Foods is the population of food sources. Each row of Foods matrix is a vector holding D parameters to be optimized. The number of rows of Foods matrix equals to the FoodNumber*/
 %                       /*汉语解释Foods[][],它代表了食物源集，二维矩阵，不难理解，每行代表某食物源的D个属性，行数FoodNumber等于食物源数量*/
 %ObjVal[FoodNumber];  /*f is a vector holding objective function values associated with food sources */
 %                     /*ObjVal[]是个向量，存储了目标函数值*/
 %Fitness[FoodNumber]; /*fitness is a vector holding fitness (quality) values associated with food sources*/
 %                     /*Fitness[]是个向量，存储了fitness 值*/
 %trial[FoodNumber]; /*trial is a vector holding trial numbers through which solutions can not be improved*/
 %                   /*trial 是个向量，证明哪个食物源没被提高*/
 %prob[FoodNumber]; /*prob is a vector holding probabilities of food sources (solutions) to be chosen*/
 %                  /*prob 存储概率*/
 %solution [D]; /*New solution (neighbour) produced by v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) j is a randomly chosen parameter and k is a randomlu chosen solution different from i*/
 %              /*solution [D] 产生一个新的食物源，用的公式跟资料上的一模一样*/
 %ObjValSol; /*Objective function value of new solution*/
 %           /*新食物源的目标函数值*/
 %FitnessSol; /*Fitness value of new solution*/
 %           /*新食物源的fitness值*/
 %neighbour, param2change; /*param2change corrresponds to j, neighbour corresponds to k in equation v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij})*/
 %           /*这两个参数对应着产生新食物源公式的j,k参数*/
 %GlobalMin; /*Optimum solution obtained by ABC algorithm*/
 %           /*利用ABC算法得到的最佳的食物源？*/
 %GlobalParams[D]; /*Parameters of the optimum solution*/
 %           /*最佳食物源参数*/
 %GlobalMins[runtime]; /*GlobalMins holds the GlobalMin of each run in multiple runs*/
 %           /**/

 GlobalMins=zeros(1,runtime);

 for r=1:runtime

 % /*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 = rand(FoodNumber,D) .* Range + Lower; %这里隐藏知识，因为+号要求左右两端横纵维数相同，所以得知，Range和Lower 都是FoodNumber行，D列，所以得知上面，ub和lb分别是1行D列的

 ObjVal=feval(objfun,Foods);
 Fitness=calculateFitness(ObjVal); %注：这里的calculateFitness并没有实现，但是源码作者想让读者明白，执行的是计算Fitness值的运算。

 %reset trial counters
 trial=zeros(1,FoodNumber);

 %/*The best food source is memorized*/
 %注：下面这个find(ObjVal==min(ObjVal)) 套式经常用，找到最小值所处的所有位置
 BestInd=find(ObjVal==min(ObjVal));
 BestInd=BestInd(end);

 GlobalMin=ObjVal(BestInd);
 GlobalParams=Foods(BestInd,:);

 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;

         %/*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,:); %sol代表solution 一个食物源,在matlab语言中 x(i，:)代表x矩阵中第i行的所有元素
        %  /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
        sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;

        %  /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
         ind=find(sol
         sol(ind)=lb(ind);
         ind=find(sol>ub);
         sol(ind)=ub(ind);

         %evaluate new solution
         ObjValSol=feval(objfun,sol);
         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;


     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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %/*注：这段代码跟楼上一样，其实在onlooker bee 阶段，做的工作跟employed bee一样的。*/
 i=1;
 t=0;
 while(t
     if(rand
         t=t+1;
         %/*The parameter to be changed is determined randomly*/
         Param2Change=fix(rand*D)+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,:);
        %  /*v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
        sol(Param2Change)=Foods(i,Param2Change)+(Foods(i,Param2Change)-Foods(neighbour,Param2Change))*(rand-0.5)*2;

        %  /*if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
         ind=find(sol
         sol(ind)=lb(ind);
         ind=find(sol>ub);
         sol(ind)=ub(ind);

         %evaluate new solution
         ObjValSol=feval(objfun,sol);
         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;
     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=ObjVal(ind);
          GlobalParams=Foods(ind,:);
          end;


 %%%%%%%%%%%% SCOUT BEE PHASE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %/*注:其实在scout bee阶段，就是根据trial counter 做处理*/
 %/*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)
     Bas(ind)=0;
     sol=(ub-lb).*rand(1,D)+lb;
     ObjValSol=feval(objfun,sol);
     FitnessSol=calculateFitness(ObjValSol);
     Foods(ind,:)=sol;
     Fitness(ind)=FitnessSol;
     ObjVal(ind)=ObjValSol;
 end;



 fprintf('ter=%d ObjVal=%g\n',iter,GlobalMin);
 iter=iter+1;

 end % End of ABC

 GlobalMins(r)=GlobalMin;
 end; %end of runs

 save all