【KELM分类】基于哈里斯算法优化核极限学习机HHO-KELM实现数据分类附matlab代码

本文链接：https://blog.youkuaiyun.com/matlab_dingdang/article/details/132589302

本文介绍了一种基于哈里斯算法优化的核极限学习机（HHO-KELM），通过哈里斯鹰优化策略改进传统KELM的权重计算，以实现对大规模数据集的更准确和高效的分类。HHO-KELM在处理大量数据时表现出更好的性能和收敛速度。

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⛄ 内容介绍

在机器学习领域，数据分类是一个重要的任务。为了提高分类算法的性能，研究人员一直在不断探索新的方法和技术。核极限学习机（KELM）是一种强大的分类算法，它在处理大规模数据集时表现出色。本文将介绍一种基于哈里斯算法优化的核极限学习机，称为HHO-KELM，以实现更准确和高效的数据分类。

首先，让我们简要了解一下核极限学习机（KELM）。KELM是一种基于极限学习机（ELM）的分类算法，它使用核函数来处理非线性分类问题。ELM是一种单层前馈神经网络，它以随机方式初始化输入层到隐藏层的连接权重，并通过最小二乘法来计算输出层到隐藏层的权重。KELM通过引入核函数，可以将ELM扩展到非线性分类问题。

然而，传统的KELM算法在处理大规模数据集时存在一些问题。由于KELM需要计算输入数据与隐藏层神经元之间的权重，当数据集非常庞大时，计算复杂度会显著增加。为了解决这个问题，研究人员提出了一种基于哈里斯算法的优化方法，即HHO-KELM。

哈里斯算法是一种基于自然进化的优化算法，它模拟了自然界中物种的进化过程。在HHO-KELM中，哈里斯算法被用来优化KELM中的权重计算过程。通过引入哈里斯算法，HHO-KELM能够更快地找到最优的权重，并且在处理大规模数据集时具有更好的性能。

HHO-KELM的基本思想是通过迭代地更新权重来最小化预测误差。在每次迭代中，哈里斯算法根据适应度函数对权重进行调整，并根据优化目标来更新权重。通过不断迭代，HHO-KELM能够逐渐优化权重，从而实现更准确和高效的数据分类。

实验结果表明，HHO-KELM在处理大规模数据集时具有显著优势。与传统的KELM算法相比，HHO-KELM能够更快地收敛，并且在准确性上表现更好。这使得HHO-KELM成为处理大规模数据分类问题的有力工具。

总结起来，基于哈里斯算法优化的核极限学习机HHO-KELM是一种强大的数据分类算法。它通过引入哈里斯算法来优化KELM中的权重计算过程，从而实现更准确和高效的数据分类。未来，我们可以进一步研究和改进HHO-KELM算法，以适应更复杂和多样化的数据分类任务。

⛄ 核心代码

% Developed in MATLAB R2013b% Source codes demo version 1.0% _____________________________________________________% Main paper:% Harris hawks optimization: Algorithm and applications% Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, Huiling Chen% Future Generation Computer Systems, % DOI: https://doi.org/10.1016/j.future.2019.02.028% https://www.sciencedirect.com/science/article/pii/S0167739X18313530% _____________________________________________________% You can run the HHO code online at codeocean.com  https://doi.org/10.24433/CO.1455672.v1% You can find the HHO code at https://github.com/aliasghar68/Harris-hawks-optimization-Algorithm-and-applications-.git% _____________________________________________________%  Author, inventor and programmer: Ali Asghar Heidari,%  PhD research intern, Department of Computer Science, School of Computing, National University of Singapore, Singapore%  Exceptionally Talented Ph. DC funded by Iran's National Elites Foundation (INEF), University of Tehran%  03-03-2019%  Researchgate: https://www.researchgate.net/profile/Ali_Asghar_Heidari%  e-Mail: as_heidari@ut.ac.ir, aliasghar68@gmail.com,%  e-Mail (Singapore): aliasgha@comp.nus.edu.sg, t0917038@u.nus.edu% _____________________________________________________%  Co-author and Advisor: Seyedali Mirjalili%%         e-Mail: ali.mirjalili@gmail.com%                 seyedali.mirjalili@griffithuni.edu.au%%       Homepage: http://www.alimirjalili.com% _____________________________________________________%  Co-authors: Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Hui-Ling Chen%       Homepage: http://www.evo-ml.com/2019/03/02/hho/% _____________________________________________________%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Harris's hawk optimizer: In this algorithm, Harris' hawks try to catch the rabbit.% T: maximum iterations, N: populatoin size, CNVG: Convergence curve% To run HHO: [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj)function [Rabbit_Energy,Rabbit_Location,CNVG]=HHO(N,T,lb,ub,dim,fobj)disp('HHO is now tackling your problem')tic% initialize the location and Energy of the rabbitRabbit_Location=zeros(1,dim);Rabbit_Energy=inf;%Initialize the locations of Harris' hawksX=initialization(N,dim,ub,lb);CNVG=zeros(1,T);t=0; % Loop counterwhile t<T    for i=1:size(X,1)        % Check boundries        FU=X(i,:)>ub;FL=X(i,:)<lb;X(i,:)=(X(i,:).*(~(FU+FL)))+ub.*FU+lb.*FL;        % fitness of locations        fitness=fobj(X(i,:));        % Update the location of Rabbit        if fitness<Rabbit_Energy            Rabbit_Energy=fitness;            Rabbit_Location=X(i,:);        end    end        E1=2*(1-(t/T)); % factor to show the decreaing energy of rabbit    % Update the location of Harris' hawks    for i=1:size(X,1)        E0=2*rand()-1; %-1<E0<1        Escaping_Energy=E1*(E0);  % escaping energy of rabbit                if abs(Escaping_Energy)>=1            %% Exploration:            % Harris' hawks perch randomly based on 2 strategy:                        q=rand();            rand_Hawk_index = floor(N*rand()+1);            X_rand = X(rand_Hawk_index, :);            if q<0.5                % perch based on other family members                X(i,:)=X_rand-rand()*abs(X_rand-2*rand()*X(i,:));            elseif q>=0.5                % perch on a random tall tree (random site inside group's home range)                X(i,:)=(Rabbit_Location(1,:)-mean(X))-rand()*((ub-lb)*rand+lb);            end                    elseif abs(Escaping_Energy)<1            %% Exploitation:            % Attacking the rabbit using 4 strategies regarding the behavior of the rabbit                        %% phase 1: surprise pounce (seven kills)            % surprise pounce (seven kills): multiple, short rapid dives by different hawks                        r=rand(); % probablity of each event                        if r>=0.5 && abs(Escaping_Energy)<0.5 % Hard besiege                X(i,:)=(Rabbit_Location)-Escaping_Energy*abs(Rabbit_Location-X(i,:));            end                        if r>=0.5 && abs(Escaping_Energy)>=0.5  % Soft besiege                Jump_strength=2*(1-rand()); % random jump strength of the rabbit                X(i,:)=(Rabbit_Location-X(i,:))-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:));            end                        %% phase 2: performing team rapid dives (leapfrog movements)            if r<0.5 && abs(Escaping_Energy)>=0.5, % Soft besiege % rabbit try to escape by many zigzag deceptive motions                                Jump_strength=2*(1-rand());                X1=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:));                                if fobj(X1)<fobj(X(i,:)) % improved move?                    X(i,:)=X1;                else % hawks perform levy-based short rapid dives around the rabbit                    X2=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-X(i,:))+rand(1,dim).*Levy(dim);                    if (fobj(X2)<fobj(X(i,:))), % improved move?                        X(i,:)=X2;                    end                end            end                        if r<0.5 && abs(Escaping_Energy)<0.5, % Hard besiege % rabbit try to escape by many zigzag deceptive motions                % hawks try to decrease their average location with the rabbit                Jump_strength=2*(1-rand());                X1=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X));                                if fobj(X1)<fobj(X(i,:)) % improved move?                    X(i,:)=X1;                else % Perform levy-based short rapid dives around the rabbit                    X2=Rabbit_Location-Escaping_Energy*abs(Jump_strength*Rabbit_Location-mean(X))+rand(1,dim).*Levy(dim);                    if (fobj(X2)<fobj(X(i,:))), % improved move?                        X(i,:)=X2;                    end                end            end            %%        end    end    t=t+1;    CNVG(t)=Rabbit_Energy;%    Print the progress every 100 iterations%    if mod(t,100)==0%        display(['At iteration ', num2str(t), ' the best fitness is ', num2str(Rabbit_Energy)]);%    endendtocend% ___________________________________function o=Levy(d)beta=1.5;sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta);u=randn(1,d)*sigma;v=randn(1,d);step=u./abs(v).^(1/beta);o=step;end