【改进灰狼优化算法】基于协调探索和开发能力的改进灰狼优化算法求解单目标优化问题（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 参考文献

🌈4 Matlab代码实现

💥1 概述

I-GWO算法受益于一种新的运动策略，称为基于维度学习的狩猎（DLH）搜索策略，该策略继承自自然界中狼的个体狩猎行为。DLH使用不同的方法为每只狼构建一个邻域，其中相邻的信息可以在狼之间共享。DLH搜索策略中使用的这种维度学习可以增强本地和全球搜索之间的平衡并保持多样性。

📚2 运行结果

部分代码：

% Improved Grey Wolf Optimizer (I-GWO)
function [Alpha_score,Alpha_pos,Convergence_curve]=IGWO(dim,N,Max_iter,lb,ub,fobj)

lu = [lb .* ones(1, dim); ub .* ones(1, dim)];

% Initialize alpha, beta, and delta positions
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 wolves
Positions=initialization(N,dim,ub,lb);
Positions = boundConstraint (Positions, Positions, lu);

% Calculate objective function for each wolf
for i=1:size(Positions,1)
Fit(i) = fobj(Positions(i,:));
end

% Personal best fitness and position obtained by each wolf
pBestScore = Fit;
pBest = Positions;

neighbor = zeros(N,N);
Convergence_curve=zeros(1,Max_iter);
iter = 0;% Loop counter

%% Main loop
while iter < Max_iter
for i=1:size(Positions,1)
fitness = Fit(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

%% Calculate the candiadate position Xi-GWO
a=2-iter*((2)/Max_iter); % a decreases linearly from 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

X_GWO(i,j)=(X1+X2+X3)/3; % Equation (3.7)

end
X_GWO(i,:) = boundConstraint(X_GWO(i,:), Positions(i,:), lu);
Fit_GWO(i) = fobj(X_GWO(i,:));
end

%% Calculate the candiadate position Xi-DLH
radius = pdist2(Positions, X_GWO, 'euclidean'); % Equation (10)
dist_Position = squareform(pdist(Positions));
r1 = randperm(N,N);

for t=1:N
neighbor(t,:) = (dist_Position(t,:)<=radius(t,t));
[~,Idx] = find(neighbor(t,:)==1); % Equation (11)
random_Idx_neighbor = randi(size(Idx,2),1,dim);

for d=1:dim
X_DLH(t,d) = Positions(t,d) + rand .*(Positions(Idx(random_Idx_neighbor(d)),d)...
- Positions(r1(t),d)); % Equation (12)
end
X_DLH(t,:) = boundConstraint(X_DLH(t,:), Positions(t,:), lu);
Fit_DLH(t) = fobj(X_DLH(t,:));
end

%% Selection
tmp = Fit_GWO < Fit_DLH; % Equation (13)
tmp_rep = repmat(tmp',1,dim);

tmpFit = tmp .* Fit_GWO + (1-tmp) .* Fit_DLH;
tmpPositions = tmp_rep .* X_GWO + (1-tmp_rep) .* X_DLH;

%% Updating
tmp = pBestScore <= tmpFit; % Equation (13)
tmp_rep = repmat(tmp',1,dim);

pBestScore = tmp .* pBestScore + (1-tmp) .* tmpFit;
pBest = tmp_rep .* pBest + (1-tmp_rep) .* tmpPositions;

Fit = pBestScore;
Positions = pBest;