【智能优化算法】基于改进收敛因子和比例权重的灰狼算法求解单目标优化问题（CGWO）matlab代码
【智能优化算法】基于改进收敛因子和比例权重的灰狼算法求解单目标优化问题（CGWO）matlab代码
TT_Matlab
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1 简介
在分析灰狼优化算法不足的基础上,提出一种改进的灰狼优化算法(CGWO),该算法采用基于余弦规律变化的收敛因子,平衡算法的全局搜索和局部搜索能力,同时引入基于步长欧氏距离的比例权重更新灰狼位置,从而加快算法的收敛速度.对8个经典测试函数进行仿真实验,结果表明CGWO算法的求解精度更高,稳定性更好.最后以预测谷氨酸菌体生长浓度为例,利用CGWO算法估计Richards模型的参数,以均方根误差和平均绝对误差作为评价指标,与PSO算法,GA算法和VS-FOA算法的结果进行比较,CGWO算法可以有效地估计Richards模型中的参数.
2 部分代码
%___________________________________________________________________%
%
Grey Wold Optimizer (GWO) source codes version 1.0 %
% %
%___________________________________________________________________%
%
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 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]王秋萍, 王梦娜, 王晓峰. 改进收敛因子和比例权重的灰狼优化算法[J]. 计算机工程与应用, 2019, 55(21):7.
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。