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⛄ 内容介绍
为了改善灰狼优化算法收敛速度慢,寻优精度低,易早熟等缺陷,提出1种改进的灰狼优化算法.在基本灰狼优化算法的基础上,引入差分进化机制生成1个变异种群,通过其动态缩放因子和交叉概率因子避免算法陷入局部最优.引入优胜劣汰的生物竞争淘汰策略,根据比较进化变异后狼群个体适应度值淘汰m只狼,同时随机生成与被淘汰狼数量相同的狼.采用典型的单峰与多峰函数对该文算法进行测试.仿真结果表明,该文算法的综合性能优于粒子群优化(PSO)和人工蜂群(ABC)等其他对比算法,提高了局部搜索的效率和精度.将该文算法应用于冷凝器实际控制参数整定优化问题中,并与遗传算法(GA),PSO和工程整定(ZN)法进行比较.仿真结果表明,该文算法整定的参数输出响应的调整时间和上升时间减小,最大超调量降低且稳定性好.
⛄ 部分代码
%___________________________________________________________________%
% Grey Wolf Optimizer (GWO) source codes version 1.0 %
% %
% Developed in MATLAB R2011b(7.13) %
% %
% Author and programmer: Seyedali Mirjalili %
% %
% e-Mail: ali.mirjalili@gmail.com %
% seyedali.mirjalili@griffithuni.edu.au %
% %
% Homepage: http://www.alimirjalili.com %
% %
% Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis %
% Grey Wolf Optimizer, Advances in Engineering %
% Software , in press, %
% DOI: 10.1016/j.advengsoft.2013.12.007 %
% %
%___________________________________________________________________%
% Grey Wolf Optimizer
function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
% initialize alpha, beta, and delta_pos
Alpha_pos=zeros(1,dim);
Alpha_score=inf; %change this to -inf for maximization problems
Beta_pos=zeros(1,dim);
Beta_score=inf; %change this to -inf for maximization problems
Delta_pos=zeros(1,dim);
Delta_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);
l=0;% Loop counter
% Main loop
while l<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 Alpha, Beta, and Delta
if fitness<Alpha_score
Alpha_score=fitness; % Update alpha
Alpha_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness<Beta_score
Beta_score=fitness; % Update beta
Beta_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score
Delta_score=fitness; % Update delta
Delta_pos=Positions(i,:);
end
end
a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0
% Update the Position of search agents including omegas
for i=1:size(Positions,1)
for j=1:size(Positions,2)
r1=rand(); % r1 is a random number in [0,1]
r2=rand(); % r2 is a random number in [0,1]
A1=2*a*r1-a; % Equation (3.3)
C1=2*r2; % Equation (3.4)
D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1
X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1
r1=rand();
r2=rand();
A2=2*a*r1-a; % Equation (3.3)
C2=2*r2; % Equation (3.4)
D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2
X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2
r1=rand();
r2=rand();
A3=2*a*r1-a; % Equation (3.3)
C3=2*r2; % Equation (3.4)
D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3
X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3
Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)
end
end
l=l+1;
Convergence_curve(l)=Alpha_score;
end
⛄ 运行结果
编辑
编辑
⛄ 参考文献
[1]朱海波, 张勇. 基于差分进化与优胜劣汰策略的灰狼优化算法[J]. 南京理工大学学报, 2018, 42(6):9.
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