1 简介
针对鲸鱼优化算法(WOA)存在收敛精度低和收敛速度慢的问题,提出基于混沌权重和精英引导的先进鲸鱼优化算法(AWOA).考虑算法前期搜索的随机性对收敛速度的影响,引入精英个体引导机制,利用精英个体的进化反馈信息及时调整种群的搜索方向,加强算法的全局搜索能力.在算法后期引入混沌动态权重因子加强算法的局部搜索能力,提高算法的收敛精度,对多个基准测试函数进行对比仿真实验,结果表明:改进的鲸鱼算法具有更高的寻优性能.
WOA 是受鲸鱼独特的泡泡网觅食行为启发而提出的，在自然界中，鲸鱼通过随机游走寻找猎物，当定位到猎物后，通过收缩螺旋包围形成泡泡网攻击猎物。通过模拟这种行为，基本的 WOA 包括三个阶段: 游走搜索猎物、收缩包围机制、螺旋包围机制。
2 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
3 仿真结果
4 参考文献
[1]黄辉先, 张广炎, 陈思溢,等. 基于混沌权重和精英引导的鲸鱼优化算法[J]. 传感器与微系统, 2020, 39(5):4.
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