【单目标优化求解】基于matlab增强型黑猩猩优化器算法求解单目标优化问题【含Matlab源码 2013期】

【单目标优化求解】基于matlab增强型黑猩猩优化器算法求解单目标优化问题【含Matlab源码 2013期】

一、黑猩猩优化算法(ChOA)简介

1 ChOA数学描述

黑猩猩优化算法(ChOA) 是M.Khi she等人于2020年根据黑猩猩群体狩猎行为提出的一种新型元启发式优化算法。ChOA通过模拟攻击黑猩猩、驱赶黑猩猩、拦截黑猩猩和追逐黑猩猩4类黑猩猩协同狩猎行为来达到求解问题的目的。与其他算法相比， ChOA具有收敛速度快、寻优精度高等特点。

(1)驱赶和追逐猎物。

在黑猩猩狩猎过程中，通常根据黑猩猩个体智力和性动机来分配狩猎职责。任何黑猩猩均可随机改变其在猎物周围空间中的位置，数学描述为

d=|cx prey(t) -mx chimp(t) |(1)

x chimp(t+1) =X prey(t) -ad(2)

式中：d为黑猩猩与猎物间距； t为当前迭代次数； X prey(t) 为猎物位置向量； X chimp(t) 为黑猩猩位置向量； a、m、c为系数向量， a=2fr 1-f， c=2r 2， m=Chaotic_value(基于混沌映射的混沌向量) ， f为迭代过程中从2.0非线性降至0， r 1、r 2为[0， 1] 范围内的随机向量。

(2)攻击方式。

黑猩猩能够探查猎物位置(通过驱赶、拦截和追逐),然后包围猎物。狩猎过程通常由攻击黑猩猩进行,驱赶黑猩猩、拦截黑猩猩和追逐黑猩猩参与狩猎过程。4类黑猩猩通过下式更新其位置,其他黑猩猩根据最佳黑猩猩位置更新其位置,猎物位置由最佳黑猩猩个体位置估计。数学描述为

式中:dAttacker、dBarrier、dChaser、dDriver分别为当前攻击黑猩猩、拦截黑猩猩、追逐黑猩猩、驱赶黑猩猩与猎物的间距;xAttacker、xBarrier、xChaser、xDriver分别为攻击黑猩猩、拦截黑猩猩、追逐黑猩猩、驱赶黑猩猩相对于猎物的位置向量;a1～a4、m1～m4、c1～c4分别为攻击黑猩猩、拦截黑猩猩、追逐黑猩猩、驱赶黑猩猩系数向量;x1、x2、x3、x4分别为攻击黑猩猩、拦截黑猩猩、追逐黑猩猩和驱赶黑猩猩位置更新向量;x为其他黑猩猩位置向量。

(3)攻击和寻找猎物。

在狩猎最后阶段,一方面黑猩猩根据攻击者、驱赶者、拦截者和追逐者位置更新位置,并攻击猎物;另一方面黑猩猩通过分散寻找猎物显示探查过程,即ChOA全局搜索。

(4)社会动机。

社会动机(修饰)会导致黑猩猩放弃其狩猎职责,这一行为有助于ChOA在求解高维问题时克服陷入局部最优和收敛速度慢等缺点。在优化过程中,通过50%的概率选择黑猩猩正常位置更新或通过混沌模型进行位置更新。数学模型表示为

式中:μ为[0,1]范围内的随机数。

二、部分源代码

close allclearclcAlgorithm_Name = 'I-ChoA';N = 30; % Number of search agentsSearchAgents_no =N;Function_name='F2'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)Max_iteration = 500; % Maximum numbef of iterations% Load details of the selected benchmark function[lb,ub,dim,fobj]=Get_Functions_details(Function_name);[ABest_scoreChimp1,ABest_posChimp1,IChoA_curve]=IChoA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);figure('Position',[500 500 660 290])%Draw search spacesubplot(1,2,1);func_plot(Function_name);title('Parameter space')xlabel('x_1');ylabel('x_2');zlabel([Function_name,'( x_1 , x_2 )'])%Draw objective spacesubplot(1,2,2);semilogy(IChoA_curve,'Color','r')title('Objective space')xlabel('Iteration');ylabel('Best score obtained so far');axis tightgrid onbox onlegend('I-ChoA')function [Attacker_score,Attacker_pos,Convergence_curve]=IChoA(N,Max_iter,lb,ub,dim,fobj)% initialize Attacker, Barrier, Chaser, and DriverAttacker_pos=zeros(1,dim);Attacker_score=inf; %change this to -inf for maximization problemsBarrier_pos=zeros(1,dim);Barrier_score=inf; %change this to -inf for maximization problemsChaser_pos=zeros(1,dim);Chaser_score=inf; %change this to -inf for maximization problemsDriver_pos=zeros(1,dim);Driver_score=inf; %change this to -inf for maximization problems%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%lu = [lb .* ones(1, dim); ub .* ones(1, dim)]; %% =========%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Initialize the positions of search agentsPositions=initialization(N,dim,ub,lb);%============================================================Positions = boundConstraint (Positions, Positions, lu); %% =====% Calculate objective function for each champfor i=1:size(Positions,1) Fit(i) = fobj(Positions(i,:));end% Personal best fitness and position obtained by each champpBestScore = Fit;pBest = Positions;neighbor = zeros(N,N);%%=======================================================================Convergence_curve=zeros(1,Max_iter);l=0;% Loop counter%%% Main loopwhile lAttacker_score && fitnessAttacker_score && fitness>Barrier_score && fitnessAttacker_score && fitness>Barrier_score && fitness>Chaser_score && fitness>Driver_score Driver_score=fitness; % Update Driver Driver_pos=Positions(i,:); end end f=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0 % The Dynamic Coefficient of f Vector as Table 1. %Group 1 C1G1=1.95-((2*l^(1/3))/(Max_iter^(1/3))); C2G1=(2*l^(1/3))/(Max_iter^(1/3))+0.5; %Group 2 C1G2= 1.95-((2*l^(1/3))/(Max_iter^(1/3))); C2G2=(2*(l^3)/(Max_iter^3))+0.5; %Group 3 C1G3=(-2*(l^3)/(Max_iter^3))+2.5; C2G3=(2*l^(1/3))/(Max_iter^(1/3))+0.5; %Group 4 C1G4=(-2*(l^3)/(Max_iter^3))+2.5; C2G4=(2*(l^3)/(Max_iter^3))+0.5; % Update the Position of search agents including omegas for i=1:size(Positions,1) for j=1:size(Positions,2) % % %% Please note that to choose a other groups you should use the related group strategies r11=C1G1*rand(); % r1 is a random number in [0,1] r12=C2G1*rand(); % r2 is a random number in [0,1] r21=C1G2*rand(); % r1 is a random number in [0,1] r22=C2G2*rand(); % r2 is a random number in [0,1] r31=C1G3*rand(); % r1 is a random number in [0,1] r32=C2G3*rand(); % r2 is a random number in [0,1] r41=C1G4*rand(); % r1 is a random number in [0,1] r42=C2G4*rand(); % r2 is a random number in [0,1] A1=2*f*r11-f; % Equation (3) C1=2*r12; % Equation (4) %% % Please note that to choose various Chaotic maps you should use the related Chaotic maps strategies m=chaos(3,1,1); % Equation (5) D_Attacker=abs(C1*Attacker_pos(j)-m*Positions(i,j)); % Equation (6) X1=Attacker_pos(j)-A1*D_Attacker; % Equation (7) A2=2*f*r21-f; % Equation (3) C2=2*r22; % Equation (4) D_Barrier=abs(C2*Barrier_pos(j)-m*Positions(i,j)); % Equation (6) X2=Barrier_pos(j)-A2*D_Barrier; % Equation (7) A3=2*f*r31-f; % Equation (3) C3=2*r32; % Equation (4) D_Driver=abs(C3*Chaser_pos(j)-m*Positions(i,j)); % Equation (6) X3=Chaser_pos(j)-A3*D_Driver; % Equation (7) A4=2*f*r41-f; % Equation (3) C4=2*r42; % Equation (4) D_Driver=abs(C4*Driver_pos(j)-m*Positions(i,j)); % Equation (6) X4=Chaser_pos(j)-A4*D_Driver; % Equation (7) X_Chimp(i,j)=(X1+X2+X3+X4)/4;% Equation (8) end X_Chimp(i,:) = boundConstraint(X_Chimp(i,:), Positions(i,:), lu); Fit_Chimp(i) = fobj(X_Chimp(i,:)); end %% Calculate the candiadate position Xi-DLH radius = pdist2(Positions, X_Chimp, 'euclidean'); % Equation (10) dist_Position = squareform(pdist(Positions)); r1 = randperm(N,N); for t=1:N neighbor(t,:) = (dist_Position(t,:)<=radius(t,t)); [~,Idx] = find(neighbor(t,:)==1); % Equation (11) random_Idx_neighbor = randi(size(Idx,2),1,dim); for d=1:dim X_DLH(t,d) = Positions(t,d) + rand .*(Positions(Idx(random_Idx_neighbor(d)),d)... - Positions(r1(t),d)); % Equation (12) end X_DLH(t,:) = boundConstraint(X_DLH(t,:), Positions(t,:), lu); Fit_DLH(t) = fobj(X_DLH(t,:)); end %% Selection tmp = Fit_Chimp < Fit_DLH; % Equation (13) tmp_rep = repmat(tmp',1,dim); tmpFit = tmp .* Fit_Chimp + (1-tmp) .* Fit_DLH; tmpPositions = tmp_rep .* X_Chimp + (1-tmp_rep) .* X_DLH; %% Updating tmp = pBestScore <= tmpFit; % Equation (13) tmp_rep = repmat(tmp',1,dim); pBestScore = tmp .* pBestScore + (1-tmp) .* tmpFit; pBest = tmp_rep .* pBest + (1-tmp_rep) .* tmpPositions; Fit = pBestScore; Positions = pBest; %% l = l+1; neighbor = zeros(N,N); Convergence_curve(l)=Attacker_score;

三、运行结果

四、matlab版本及参考文献

1 matlab版本 2014a

2 参考文献 [1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）[M].电子工业出版社，2016. [2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社，2017. [3]程国森,崔东文.黑猩猩优化算法-极限学习机模型在富水性分级判定中的应用[J].人民黄河. 2021,43(07)

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