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1简介

基于非线性收敛因子改进的灰狼优化算法(GWO-S)进行8组标准测试函数的实验测试,结果表明

,GWO-S能在较短的时间寻优.

2部分代码

%___________________________________________________________________%

%GreyWolfOptimizer

function[Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

%initializealpha,beta,anddelta_pos

Alpha_pos=zeros(1,dim);

Alpha_score=inf;%changethisto-infformaximizationproblems

Beta_pos=zeros(1,dim);

Beta_score=inf;%changethisto-infformaximizationproblems

Delta_pos=zeros(1,dim);

Delta_score=inf;%changethisto-infformaximizationproblems

%Initializethepositionsofsearchagents

Positions=initialization(SearchAgents_no,dim,ub,lb);

Convergence_curve=zeros(1,Max_iter);

l=0;%Loopcounter

%Mainloop

whilelMax_iter

fori=1:size(Positions,1)

%Returnbackthesearchagentsthatgobeyondtheboundariesofthesearchspace

Flag4ub=Positions(i,:)ub;

Flag4lb=Positions(i,:)lb;

Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;

%Calculateobjectivefunctionforeachsearchagent

fitness=fobj(Positions(i,:));

%UpdateAlpha,Beta,andDelta

iffitnessAlpha_score

Alpha_score=fitness;%Updatealpha

Alpha_pos=Positions(i,:);

end

iffitnessAlpha_scorefitnessBeta_score

Beta_score=fitness;%Updatebeta

Beta_pos=Positions(i,:);

end

iffitnessAlpha_scorefitnessBeta_scorefitnessDelta_score

Delta_score=fitness;%Updatedelta

Delta_pos=Positions(i,:);

end

end

%adecreaseslinearlyfron2to0

a=sin(((l*pi)/Max_iter)+pi/2)+1;

%UpdatethePositionofsearchagentsincludingomegas

fori=1:size(Positions,1)

forj=1:size(Positions,2)

r1=rand();%r1isarandomnumberin[0,1]

r2=rand();%r2isarandomnumberin[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)-part1

X1=Alpha_pos(j)-A1*D_alpha;%Equation(3.6)-part1

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)-part2

X2=Beta_pos(j)-A2*D_beta;%Equation(3.6)-part2

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)-part3

X3=Delta_pos(j)-A3*D_delta;%Equation(3.5)-part3

Positions(i,j)=(X1+X2+X3)/3;%Equation(3.7)

end

end

l=l+1;

Convergence_curve(l)=Alpha_score;

end

3仿真结果4参考文献

[1]王正通,尤文,李双.改进非线性收敛因子灰狼优化算法[J].长春工业大学学报：自然科学版,,41(2):6.

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