【改进灰狼优化算法】改进收敛因子和比例权重的灰狼优化算法【期刊论文完美复现】（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
📚2 运行结果
🎉3 文献来源
🌈4 Matlab代码实现

💥1 概述

文献来源：

摘要：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。

关键词：

灰狼优化算法;收敛因子;Richards模型;参数估计;

📚2 运行结果

部分代码：

function [Alpha_score,Alpha_pos,Convergence_curve]=CGWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

%% 收敛因子参数

aintit = 2;

afinal = 0;

% 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

%% 改进点：收敛因子改进，文献中式（7）

n = 1;%递减系数

if(l<0.5*Max_iter)

a = afinal + (aintit - afinal)*(1 + (cos((l-1)*pi/( Max_iter-1)))^n)/2;

else

a = afinal + (aintit - afinal)*(1 - (cos((l-1)*pi/( Max_iter-1)))^n)/2;

end

% 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

%% 改进点：基于步长欧氏距离的比例权重

S = abs(X1) + abs(X2)+abs(X3);

if S~=0 %防止分母为0

W1 = abs(X1)/(abs(X1) + abs(X2)+abs(X3));

W2 = abs(X2)/(abs(X1) + abs(X2)+abs(X3));

W3 = abs(X3)/(abs(X1) + abs(X2)+abs(X3));

else

W1=1;W2=1;W3=1;

end

Positions(i,j)=(W1*X1+X2*W2+X3*W3)/3;% Equation (3.7)

end

end

l=l+1;

Convergence_curve(l)=Alpha_score;

end

🎉3 文献来源

部分理论来源于网络，如有侵权请联系删除。

[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):60-65+98.

🌈4 Matlab代码实现