【灰狼算法】基于Iterative映射和单纯形法改进灰狼优化算法求解单目标优化问题(SMIGWO)含Matlab源码

1 简介

为了解决基本灰狼优化算法(GWO)依赖初始种群和求解精度不高的问题,提出一种基于Iterative映射和单纯形法的改进灰狼优化算法(SMIGWO).该算法利用混沌Iterative映射产生初始灰狼种群,增强全局搜索过程中的种群多样性;采用逆不完全Γ函数更新收敛因子,以平衡算法的全局搜索和局部搜索能力;利用单纯形法的反射,扩张和收缩操作对当前较差个体进行改进,避免算法陷入局部最优.对10个测试函数进行仿真实验,数值结果表明,与基本GWO算法,改进的灰狼优化算法求解精度更高,稳定性更好.

2 部分代码

%___________________________________________________________________%%  Grey Wolf 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=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0        % 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]王梦娜, 王秋萍, 王晓峰. 基于Iterative映射和单纯形法的改进灰狼优化算法[J]. 计算机应用, 2018, 38(A02):6.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。