基于改进人工蜂群算法的K均值聚类算法（Matlab代码实现）

news2024/11/28 22:44:32

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🍁🥬🕒摘要🕒🥬🍁

人工蜂群（Artificial Bee Colony, ABC）算法作为一种新型的群智能优化算法,近十年发展十分迅速。算法的生物模型是受到蜜蜂在采蜜过程中,蜂群所表现出来的相互协作的智能行为的启发。通过对整个采蜜过程的抽象,提炼出人工蜂群算法,用来解决现实生活中的实际问题。由于人工蜂群算法具有算法实现简单、搜索精度高、鲁棒性较强等特点,且与经典的优化算法相比求解质量较好等,2005年由土耳其学者Karaboga提出,很快引起了众多学者的广泛关注,人工蜂群算法已经应用于旅行商问题、人工神经网络、无线传感器网络节点部署、调度问题等众多领域,并且取得了较好的成果,研究者们还在试图将算法应用到更多新的领域。人工蜂群算法作为一种新型的算法,算法还处于初级阶段,算法模型还不是很完善,在应用时也会表现出许多不足之处,在面对比较复杂的优化问题的时,算法容易“早熟”和陷入局部最优等问题。因此,研究者们开始探究如何在理论上对基本人工蜂群算法改进,在应用领域方面拓展其适用范围等问题。

✨🔎⚡运行结果⚡🔎✨

💂♨️👨‍🎓Matlab代码👨‍🎓♨️💂

a=3;
figure;
GlobalMins=runABC(a);
semilogy(GlobalMins,'k:');
%semilogy(mean(GlobalMins))
title('griewank函数的适应度值收敛趋势');
xlabel('迭代次数（cycles）');
ylabel('适应度（fitness）');
b=3;
GlobalMins=runABCimprove(b);
GlobalMins1=GlobalMins;
hold on;
semilogy(GlobalMins1,'k-');
legend('原始蜂群算法','本文算法');

%%%%%ARTIFICIAL BEE COLONY ALGORITHM%%%%

%Artificial Bee Colony Algorithm was developed by Dervis Karaboga in 2005
%by simulating the foraging behaviour of bees.

%Copyright ?2008 Erciyes University, Intelligent Systems Research Group, The Dept. of Computer Engineering

%Contact:
%Dervis Karaboga (karaboga@erciyes.edu.tr )
%Bahriye Basturk Akay (bahriye@erciyes.edu.tr)
function GlobalMins=runABC(a)

% Set ABC Control Parameters
ABCOpts = struct( 'ColonySize', 20, ... % Number of Employed Bees+ Number of Onlooker Bees
'MaxCycles', 2000,... % Maximum cycle number in order to terminate the algorithm
'ErrGoal', 1e-20, ... % Error goal in order to terminate the algorithm (not used in the code in current version)
'Dim', 2 , ... % Number of parameters of the objective function
'Limit', 100, ... % Control paramter in order to abandone the food source
'lb', -3, ... % Lower bound of the parameters to be optimized
'ub', 3, ... %Upper bound of the parameters to be optimized
'ObjFun' , 'griewank', ... %Write the name of the objective function you want to minimize
'RunTime',1); % Number of the runs

GlobalMins=zeros(ABCOpts.RunTime,ABCOpts.MaxCycles);

for r=1:ABCOpts.RunTime

% Initialise population
Range = repmat((ABCOpts.ub-ABCOpts.lb),[ABCOpts.ColonySize ABCOpts.Dim]);
Lower = repmat(ABCOpts.lb, [ABCOpts.ColonySize ABCOpts.Dim]);
Colony = rand(ABCOpts.ColonySize,ABCOpts.Dim) .* Range + Lower;%生成初始Colony，其中ColonySize行，Dim列，10*5
%zj先初始化种群规模。。。这个就是算法中式子：x(j)i=x(j)min+rand(0,1)(x(j)max-x(j)min)

Employed=Colony(1:(ABCOpts.ColonySize/2),:);%前一半为引领蜂或食物源，5*5
%zj再将种群的前一半作为引领蜂规模