【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
【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
TT_Matlab
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，完整matlab代码或者程序定制加qq1575304183。
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: %
%
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 Optimizer
function
[Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
%
initialize alpha, beta, and delta_pos
Alpha_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 agents
Positions
=
initialization(SearchAgents_no,dim,ub,lb);
Convergence_curve
=
zeros(1,Max_iter);
l
=
0;% Loop counter
%
Main loop
while
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。