【灰狼算法】基于自适应头狼的灰狼优化算法求解单目标优化问题（ALGWO）matlab代码

1 简介

灰狼优化算法一种模拟灰狼捕食行为的元启发式优化算法.由于灰狼算法在种群迭代更新中始终靠近最优解,所以易陷入局部最优.提出了一种基于自适应头狼的灰狼优化算法,并在个体迭代更新中选择合适的头狼个数进行个体更新,这使得算法能够平衡开发和勘探能力.通过对20个基准函数优化问题的仿真实验表明,改进后的算法与原始灰狼优化算法相比,其全局搜索能力有显著提高.

1.1 灰狼算法介绍

​2 部分代码

%___________________________________________________________________%%  Grey Wold Optimizer (GWO) source codes version 1.0               %%                                                                   %%  Developed in MATLAB R2011b(7.13)                                 %%                                                                   %%  Author and programmer: Seyedali Mirjalili                        %%                                                                   %%         e-Mail: ali.mirjalili@gmail.com                           %%                 seyedali.mirjalili@griffithuni.edu.au             %%                                                                   %%       Homepage: http://www.alimirjalili.com                       %%                                                                   %%   Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis             %%               Grey Wolf Optimizer, Advances in Engineering        %%               Software , in press,                                %%               DOI: 10.1016/j.advengsoft.2013.12.007               %%                                                                   %%___________________________________________________________________%​% Grey Wolf Optimizerfunction [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)​% initialize alpha, beta, and delta_posAlpha_pos=zeros(1,dim);Alpha_score=inf; %change this to -inf for maximization problems​Beta_pos=zeros(1,dim);Beta_score=inf; %change this to -inf for maximization problems​Delta_pos=zeros(1,dim);Delta_score=inf; %change this to -inf for maximization problems​%Initialize the positions of search agentsPositions=initialization(SearchAgents_no,dim,ub,lb);​Convergence_curve=zeros(1,Max_iter);​l=0;% Loop counter​% Main loopwhile l<Max_iter    for i=1:size(Positions,1)                 % Return back the search agents that go beyond the boundaries of the search space        Flag4ub=Positions(i,:)>ub;        Flag4lb=Positions(i,:)<lb;        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                               % Calculate objective function for each search agent        fitness=fobj(Positions(i,:));                % Update Alpha, Beta, and Delta        if fitness<Alpha_score             Alpha_score=fitness; % Update alpha            Alpha_pos=Positions(i,:);        end                if fitness>Alpha_score && fitness<Beta_score             Beta_score=fitness; % Update beta            Beta_pos=Positions(i,:);        end                if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score             Delta_score=fitness; % Update delta            Delta_pos=Positions(i,:);        end    end            % a decreases linearly fron 2 to 0     a=sin(((l*pi)/Max_iter)+pi/2)+1;    % Update the Position of search agents including omegas    for i=1:size(Positions,1)        for j=1:size(Positions,2)                                        r1=rand(); % r1 is a random number in [0,1]            r2=rand(); % r2 is a random number in [0,1]                        A1=2*a*r1-a; % Equation (3.3)            C1=2*r2; % Equation (3.4)                        D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1            X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1                                   r1=rand();            r2=rand();                        A2=2*a*r1-a; % Equation (3.3)            C2=2*r2; % Equation (3.4)                        D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2            X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2                               r1=rand();            r2=rand();                         A3=2*a*r1-a; % Equation (3.3)            C3=2*r2; % Equation (3.4)                        D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3                                     Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)                    end    end    l=l+1;        Convergence_curve(l)=Alpha_score;end​​​​

3 仿真结果

4 参考文献

[1]郭阳, 张涛, 胡玉蝶,等. 基于自适应头狼的灰狼优化算法[J]. 成都大学学报：自然科学版, 2020, 39(1):5.

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