人工蜂群算法matlab蜂群种群大小怎么设定

Posted 2023-04-06

参考技术A %/* ABC algorithm coded using MATLAB language */

%/* Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. */

%/* Referance Papers*/

%/*D. Karaboga, AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION,TECHNICAL REPORT-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 2005.*/

%/*D. Karaboga, B. Basturk, A powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm, Journal of Global Optimization, Volume:39, Issue:3,pp:459-171, November 2007,ISSN:0925-5001 , doi: 10.1007/s10898-007-9149-x */

%/*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. */

%/*D. Karaboga, B. Akay, A Comparative Study of Artificial Bee Colony Algorithm, Applied Mathematics and Computation, 214, 108-132, 2009. */

%/*Copyright ?2009 Erciyes University, Intelligent Systems Research Group, The Dept. of Computer Engineering*/

%/*Contact:
%Dervis Karaboga ([e-mail] )
%Bahriye Basturk Akay ([e-mail])
%*/

clear all
close all
clc

%/* Control Parameters of ABC algorithm*/
NP=20; %/* The number of colony size (employed bees+onlooker bees)*/
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=2500; %/*The number of cycles for foraging a stopping criteria*/

%/* Problem specific variables*/
objfun='Sphere'; %cost function to be optimized
D=100; %/*The number of parameters of the problem to be optimized*/
ub=ones(1,D)*100; %/*lower bounds of the parameters. */
lb=ones(1,D)*(-100);%/*upper bound of the parameters.*/

runtime=1;%/*Algorithm can be run many times in order to see its robustness*/

%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*/
%ObjVal[FoodNumber]; /*f is a vector holding objective function values associated with food sources */
%Fitness[FoodNumber]; /*fitness is a vector holding fitness (quality) values associated with food sources*/
%trial[FoodNumber]; /*trial is a vector holding trial numbers through which solutions can not be improved*/
%prob[FoodNumber]; /*prob is a vector holding probabilities of food sources (solutions) to be chosen*/
%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*/
%ObjValSol; /*Objective function value of new solution*/
%FitnessSol; /*Fitness value of new solution*/
%neighbour, param2change; /*param2change corrresponds to j, neighbour corresponds to k in equation v_ij=x_ij+\phi_ij*(x_kj-x_ij)*/
%GlobalMin; /*Optimum solution obtained by ABC algorithm*/
%GlobalParams[D]; /*Parameters of the optimum solution*/
%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;

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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%/* 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;
%/*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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%/*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('Ýter=%d ObjVal=%g\n',iter,GlobalMin);
iter=iter+1;

end % End of ABC

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

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人工蜂群优化算法原理测试函数matlab代码

 一、算法原理  

        人工蜂群算法(ABC)是模仿蜜蜂在自然环境中搜寻蜜源时蜂群个体间分工相互协作的群体行为。在一个完整的搜索过程中，必须同时进行探索和开采过程。引领蜂负责随机搜寻任务，跟随蜂执行开发任务，侦察蜂负责探测任务，在随机搜索蜜源期间，每只引领蜂在设置初始蜜源位置后，均在其附近不断搜索更优质蜜源。选择较优蜜源后，标记蜜源，并返回蜂巢附近的跳舞区域等待跟随蜂，通过跳舞共享有关该蜜源质量的信息。跟随蜂在等待区域观察引领蜂舞蹈隐含的蜜源质量信息，据此选择较优的蜜源，进一步搜寻，直至找到最优蜜源。同时高质量蜜源周围跟随蜂数量较多，有利于优质蜜源的开采。当蜜源所在位置已经被充分探索时，跟随蜂会放弃该蜜源，改变其身份，成为侦察蜂，重新搜索新的食物源。人工蜂群算法的步骤被概括为三个阶段：引领蜂随机探索蜜源、跟随蜂搜寻最优蜜源以及侦查蜂探寻蜜源[1]。

        人工蜂群算法的具体描述如下:

二、测试函数

单峰测试函数

多锋测试函数

全局最优值均为x*=0

三、测试效果

测试函数1

 

 测试函数2

 四、代码

人工蜂群matlab代码.rar-专业指导文档类资源-CSDN下载

[1]王奕丹. 蜂群算法优化改进及其在聚类中的应用研究[D].长春工业大学,2020.