标签：function dim 黏菌 lb --- fobj end 智能算法 ub
一种新的群智能算法—黏菌算法
介绍一种新的群智能算法—黏菌算法
近些年群智能算法由于其效率较高，使用方便的优点引起了广大科研者的关注与兴趣。最近看文献，温州大学的李世民（现在去复旦读研究生了）提出了一种新的群智能优化算法----黏菌算法（Slime mould algorithm）[1]。在此附上代码
算法（Matlab实现）
SMA.m
% Max_iter: maximum iterations, N: populatoin size, Convergence_curve: Convergence curve
% To run SMA: [Destination_fitness,bestPositions,Convergence_curve]=SMA(N,Max_iter,lb,ub,dim,fobj)
function [Destination_fitness,bestPositions,Convergence_curve]=SMA(N,Max_iter,lb,ub,dim,fobj)
disp('SMA is now tackling your problem')
% initialize position
bestPositions=zeros(1,dim);
Destination_fitness=inf;%change this to -inf for maximization problems
AllFitness = inf*ones(N,1);%record the fitness of all slime mold
weight = ones(N,dim);%fitness weight of each slime mold
%Initialize the set of random solutions
X=initialization(N,dim,ub,lb);
Convergence_curve=zeros(1,Max_iter);
it=1; %Number of iterations
lb=ones(1,dim).*lb; % lower boundary
ub=ones(1,dim).*ub; % upper boundary
z=0.03; % parameter
% Main loop
while it <= Max_iter
%sort the fitness
for i=1:N
% Check if solutions go outside the search space and bring them back
Flag4ub=X(i,:)>ub;
Flag4lb=X(i,:)<lb;
X(i,:)=(X(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
AllFitness(i) = fobj(X(i,:));
end
[SmellOrder,SmellIndex] = sort(AllFitness); %Eq.(2.6)
worstFitness = SmellOrder(N);
bestFitness = SmellOrder(1);
S=bestFitness-worstFitness+eps; % plus eps to avoid denominator zero
%calculate the fitness weight of each slime mold
for i=1:N
for j=1:dim
if i<=(N/2) %Eq.(2.5)
weight(SmellIndex(i),j) = 1+rand()*log10((bestFitness-SmellOrder(i))/(S)+1);
else
weight(SmellIndex(i),j) = 1-rand()*log10((bestFitness-SmellOrder(i))/(S)+1);
end
end
end
%update the best fitness value and best position
if bestFitness < Destination_fitness
bestPositions=X(SmellIndex(1),:);
Destination_fitness = bestFitness;
end
a = atanh(-(it/Max_iter)+1); %Eq.(2.4)
b = 1-it/Max_iter;
% Update the Position of search agents
for i=1:N
if rand<z %Eq.(2.7)
X(i,:) = (ub-lb)*rand+lb;
else
p =tanh(abs(AllFitness(i)-Destination_fitness)); %Eq.(2.2)
vb = unifrnd(-a,a,1,dim); %Eq.(2.3)
vc = unifrnd(-b,b,1,dim);
for j=1:dim
r = rand();
A = randi([1,N]); % two positions randomly selected from population
B = randi([1,N]);
if r<p %Eq.(2.1)
X(i,j) = bestPositions(j)+ vb(j)*(weight(i,j)*X(A,j)-X(B,j));
else
X(i,j) = vc(j)*X(i,j);
end
end
end
end
Convergence_curve(it)=Destination_fitness;
it=it+1;
end
end
Main.m
// An highlighted block
clear all
close all
clc
N=30; % Number of search agents
Function_name='F1'; % Name of the test function, range from F1-F13
T=500; % Maximum number of iterations
dimSize = 30; %dimension size
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_SMA(Function_name,dimSize);
[Destination_fitness,bestPositions,Convergence_curve]=SMA(N,T,lb,ub,dim,fobj);
%Draw objective space
figure,
hold on
semilogy(Convergence_curve,'Color','b','LineWidth',4);
title('Convergence curve')
xlabel('Iteration');
ylabel('Best fitness obtained so far');
axis tight
grid off
box on
legend('SMA')
display(['The best location of SMA is: ', num2str(bestPositions)]);
display(['The best fitness of SMA is: ', num2str(Destination_fitness)]);
Initialization.m
% This function initialize the first population of search agents
function Positions=initialization(SearchAgents_no,dim,ub,lb)
Boundary_no= size(ub,2); % numnber of boundaries
% If the boundaries of all variables are equal and user enter a signle
% number for both ub and lb
if Boundary_no==1
Positions=rand(SearchAgents_no,dim).*(ub-lb)+lb;
end
% If each variable has a different lb and ub
if Boundary_no>1
for i=1:dim
ub_i=ub(i);
lb_i=lb(i);
Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i;
end
end
Get_Functions_SMA.m
function [lb,ub,dim,fobj] = Get_Functions_SMA(F,DimValue)
switch F
case 'F1'
fobj = @F1;
lb=-100;
ub=100;
dim=DimValue;
case 'F2'
fobj = @F2;
lb=-10;
ub=10;
dim=DimValue;
case 'F3'
fobj = @F3;
lb=-100;
ub=100;
dim=DimValue;
case 'F4'
fobj = @F4;
lb=-100;
ub=100;
dim=DimValue;
case 'F5'
fobj = @F5;
lb=-30;
ub=30;
dim=DimValue;
case 'F6'
fobj = @F6;
lb=-100;
ub=100;
dim=DimValue;
case 'F7'
fobj = @F7;
lb=-1.28;
ub=1.28;
dim=DimValue;
case 'F8'
fobj = @F8;
lb=-500;
ub=500;
dim=DimValue;
case 'F9'
fobj = @F9;
lb=-5.12;
ub=5.12;
dim=DimValue;
case 'F10'
fobj = @F10;
lb=-32;
ub=32;
dim=DimValue;
case 'F11'
fobj = @F11;
lb=-600;
ub=600;
dim=DimValue;
case 'F12'
fobj = @F12;
lb=-50;
ub=50;
dim=DimValue;
case 'F13'
fobj = @F13;
lb=-50;
ub=50;
dim=DimValue;
end
end
% F1
function o = F1(x)
o=sum(x.^2);
end
% F2
function o = F2(x)
o=sum(abs(x))+prod(abs(x));
end
% F3
function o = F3(x)
dim=size(x,2);
o=0;
for i=1:dim
o=o+sum(x(1:i))^2;
end
end
% F4
function o = F4(x)
o=max(abs(x));
end
% F5
function o = F5(x)
dim=size(x,2);
o=sum(100*(x(2:dim)-(x(1:dim-1).^2)).^2+(x(1:dim-1)-1).^2);
end
% F6
function o = F6(x)
o=sum(abs((x+.5)).^2);
end
% F7
function o = F7(x)
dim=size(x,2);
o=sum([1:dim].*(x.^4))+rand;
end
% F8
function o = F8(x)
o=sum(-x.*sin(sqrt(abs(x))));
end
% F9
function o = F9(x)
dim=size(x,2);
o=sum(x.^2-10*cos(2*pi.*x))+10*dim;
end
% F10
function o = F10(x)
dim=size(x,2);
o=-20*exp(-.2*sqrt(sum(x.^2)/dim))-exp(sum(cos(2*pi.*x))/dim)+20+exp(1);
end
% F11
function o = F11(x)
dim=size(x,2);
o=sum(x.^2)/4000-prod(cos(x./sqrt([1:dim])))+1;
end
% F12
function o = F12(x)
dim=size(x,2);
o=(pi/dim)*(10*((sin(pi*(1+(x(1)+1)/4)))^2)+sum((((x(1:dim-1)+1)./4).^2).*...
(1+10.*((sin(pi.*(1+(x(2:dim)+1)./4)))).^2))+((x(dim)+1)/4)^2)+sum(Ufun(x,10,100,4));
end
% F13
function o = F13(x)
dim=size(x,2);
o=.1*((sin(3*pi*x(1)))^2+sum((x(1:dim-1)-1).^2.*(1+(sin(3.*pi.*x(2:dim))).^2))+...
((x(dim)-1)^2)*(1+(sin(2*pi*x(dim)))^2))+sum(Ufun(x,5,100,4));
end
function o=Ufun(x,a,k,m)
o=k.*((x-a).^m).*(x>a)+k.*((-x-a).^m).*(x<(-a));
end
参考文献
[1]: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems.
[2]: http://www.aliasgharheidari.com/SMA.html
[3]:https://www.researchgate.net/publication/340527543_Matlab_code_of_Slime_Mould_Algorithm_SMA
标签：function,dim,黏菌,lb,---,fobj,end,智能算法,ub 来源： https://blog.csdn.net/reliability2324/article/details/110006471
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。