# 【鲸鱼优化算法】基于阈值控制的鲸鱼算法TIWOA求解单目标优化问题附matlab代码

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## ⛄ 部分代码

%_________________________________________________________________________%

% 鲸鱼优化算法             %

%_________________________________________________________________________%

% The Whale Optimization Algorithm

function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% initialize position vector and score for the leader

Leader_pos=zeros(1,dim);

Leader_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);

t=0;% Loop counter

% Main loop

while t<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 the leader

if fitness<Leader_score % Change this to > for maximization problem

Leader_score=fitness; % Update alpha

Leader_pos=Positions(i,:);

end

end

a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)

% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)

a2=-1+t*((-1)/Max_iter);

% Update the Position of search agents

for i=1:size(Positions,1)

r1=rand(); % r1 is a random number in [0,1]

r2=rand(); % r2 is a random number in [0,1]

A=2*a*r1-a;  % Eq. (2.3) in the paper

C=2*r2;      % Eq. (2.4) in the paper

b=1;               %  parameters in Eq. (2.5)

l=(a2-1)*rand+1;   %  parameters in Eq. (2.5)

p = rand();        % p in Eq. (2.6)

for j=1:size(Positions,2)

if p<0.5

if abs(A)>=1

rand_leader_index = floor(SearchAgents_no*rand()+1);

X_rand = Positions(rand_leader_index, :);

D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)

Positions(i,j)=X_rand(j)-A*D_X_rand;      % Eq. (2.8)

elseif abs(A)<1

D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)

Positions(i,j)=Leader_pos(j)-A*D_Leader;      % Eq. (2.2)

end

elseif p>=0.5

distance2Leader=abs(Leader_pos(j)-Positions(i,j));

% Eq. (2.5)

Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j);

end

end

end

t=t+1;

Convergence_curve(t)=Leader_score;

end

## ⛄ 参考文献

[1]黄飞, 吴泽忠. 基于阈值控制的一种改进鲸鱼算法[J]. 系统工程, 2020, 38(2):16.

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