【鲸鱼算法】基于收敛因子非线性变化的鲸鱼优化算法
(IWOA) 求解单目标优化问题附matlab代码
1 简介
针对基本鲸鱼优化算法在处理复杂全局优化问题时存在解精度低和收敛速度慢等缺点,提出一种收敛因子
随进化迭代次数非线性变化的改进鲸鱼优化算法.该算法利用混沌方法替代随机方法初始化种群,使群体具
有较好的多样性.受粒子群算法惯性权重启发,设计出一种随进化迭代次数增加而非线性变化的收敛因子更
新公式,以平衡算法的全局搜索和局部搜索能力.对当前最优鲸鱼个体执行混沌扰动策略以扩大其搜索范
围.选取6个高维标准测试函数进行数值实验,结果表明该算法具有较高的收敛精度和较快的收敛速度.
2 部分代码
%_________________________________________________________________________%% 鲸鱼
优化算法
%%_________________________________________________________________________%% The
Whale Optimization Algorithmfunction
[Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,d
im,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
agentsPositions=initialization(SearchAgents_no,dim,ub,lb);Convergence_curve=zero
s(1,Max_iter);t=0;% Loop counter% Main loopwhile 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
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