【智能优化算法】基于混沌权重和精英引导的鲸鱼优化算法(AWOA)求解单目标优化问题附matlab代码

2022-02-09 15:58:18 阅读：286 来源： 互联网

标签：rand Eq Positions 算法 matlab 鲸鱼优化 Leader

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

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

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.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。

标签：rand,Eq,Positions,算法,matlab,鲸鱼,优化,Leader
来源： https://blog.csdn.net/qq_59747472/article/details/122843674