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⛄ 内容介绍
为了改善鲸鱼优化算法(WOA)的不足,如容易陷入局部最优,收敛速度慢等问题,本文提出了改进鲸鱼优化算法.通过混沌Tent映射随机生成算法的初始种群位置,让种群分布更均匀,加快算法的收敛速度;.实验部分通过对基准函数仿真仿真结果表明:本文所提改进算法具有良好的有效性和优越性.
⛄ 部分代码
function [Leader_score,Leader_pos,Convergence_curve]=IWOA(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
%×?·??ò??????
Positions=initializationNew(SearchAgents_no,dim,ub,lb,fobj);
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
%·??????????ò×?
a=2 - sin(t*pi/(2*Max_iter) + 0);
% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)
a2=-1+t*((-1)/Max_iter);
%×??????¨??
w = 1 - (exp(t/Max_iter) - 1)/(exp(1) -1);
% 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)=w*X_rand(j)-A*D_X_rand; % ?????¨??
elseif abs(A)<1
D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)
Positions(i,j)=w*Leader_pos(j)-A*D_Leader; % ?????¨??
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)+w*Leader_pos(j); % ?????¨??
end
end
%±??????í
Flag4ub=Positions(i,:)>ub;
Flag4lb=Positions(i,:)<lb;
Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
%???ú??·?±??ì
Rindex = randi(SearchAgents_no);%???ú????????????
r1 = rand; r2 = rand;
Temp = r1.*(Leader_pos - Positions(i,:)) + r2.*(Positions(Rindex,:) - Positions(i,:));
Flag4ub=Temp>ub;
Flag4lb=Temp<lb;
Temp=(Temp.*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
if fobj(Temp) < fobj(Positions(i,:))
Positions(i,:) = Temp;
end
end
t=t+1;
Convergence_curve(t)=Leader_score;
end
⛄ 运行结果
⛄ 参考文献
[1] 林杰, 何庆, 王茜,等. 基于混沌的正余弦鲸鱼优化算法[J]. 智能计算机与应用, 2020.
[2] 马晓宁, 李笑含. 基于Tent混沌映射的可复制的鲸鱼算法[J]. 计算机仿真, 2022(008):039.
