基于人工蜂群算法的线性规划问题求解.zip

close all clc clear %% NP=20; %/* The number of colony size (employed bees+onlooker bees)*/f蜂群大小 FoodNumber=NP/2; %/*The number of food sources equals the half of the colony size*/ limit=100; %/*A food source which could not be improved through "limit" trials is abandoned by its employed bee*/ maxCycle=300; %/*The number of cycles for foraging {a stopping criteria}*/ %/* Problem specific variables*/ objfun='Sphere'; %cost function to be optimized29 / 成本函数有待优化 D=3; %/*The number of parameters of the problem to be optimized*/要优化的问题的参数数量 ub=ones(1,D)*15; %/*lower bounds of the parameters. */参数的下限 lb=ones(1,D)*0;%/*upper bound of the parameters.*/ runtime=1;%/*Algorithm can be run many times in order to see its robustness*/ 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; ObjVal=feval(objfun,Foods); 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,:); 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,:); % /*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<lb); 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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; %/*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<lb); 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) GlobalMin=ObjVal(ind); GlobalParams=Foods(ind,:); end; %% %%%%%%%%%% SCOUT BEE PHASE 侦查蜂阶段%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ind=find(trial==max(trial)); ind=ind(end); if (trial(ind)>limit) trial(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; BestCost(iter)=GlobalMin; fprintf('iter=%d,ObjVal=%g\n',iter,GlobalMin); plot(BestCost(1:iter)); xlabel('迭代次数'); ylabel('适应度'); drawnow iter=iter+1; end % End of ABC GlobalMins(r)=GlobalMin; end %end of runs %% disp('输出结果') disp('最优变量') sol disp('最优值') GlobalMin