🍁🥬🕒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
📜📢🌈参考文献🌈📢📜
[1]黄媛媛. 一种改进的人工蜂群算法及其在k均值聚类中的应用[D].安徽大学,2015.
