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【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码

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1 简介 ​ 编辑 ​ 编辑 正在上传…重新上传取消 ​ 编辑 2 部分代码 %_________________________________________________________________________________ % Salp Swarm Algorithm (SSA) source codes version 1.0 % % Main pap


1 简介

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_d3

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_d3_02编辑

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_参考文献_03

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_参考文献_04编辑

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_上传_05正在上传…重新上传取消

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_参考文献_06

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_参考文献_07编辑

2 部分代码

%_________________________________________________________________________________
% Salp Swarm Algorithm (SSA) source codes version 1.0
%
% Main paper:
% S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili,
% Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems
% Advances in Engineering Software
% DOI: http://dx.doi.org/10.1016/j.advengsoft.2017.07.002
%____________________________________________________________________________________
function [FoodFitness,FoodPosition,Convergence_curve]=SSA(N,Max_iter,lb,ub,dim,fobj)
if size(ub,1)==1
ub=ones(dim,1)*ub;
lb=ones(dim,1)*lb;
end
Convergence_curve = zeros(1,Max_iter);
%Initialize the positions of salps
SalpPositions=initialization(N,dim,ub,lb);
FoodPosition=zeros(1,dim);
FoodFitness=inf;
%calculate the fitness of initial salps
for i=1:size(SalpPositions,1)
SalpFitness(1,i)=fobj(SalpPositions(i,:));
end
[sorted_salps_fitness,sorted_indexes]=sort(SalpFitness);
for newindex=1:N
Sorted_salps(newindex,:)=SalpPositions(sorted_indexes(newindex),:);
end
FoodPosition=Sorted_salps(1,:);
FoodFitness=sorted_salps_fitness(1);
%Main loop
l=2; % start from the second iteration since the first iteration was dedicated to calculating the fitness of salps
while l<Max_iter+1
c1 = 2*exp(-(4*l/Max_iter)^2); % Eq. (3.2) in the paper
for i=1:size(SalpPositions,1)
SalpPositions= SalpPositions';
if i<=N/2
for j=1:1:dim
c2=rand();
c3=rand();
%%%%%%%%%%%%% % Eq. (3.1) in the paper %%%%%%%%%%%%%%
if c3<0.5
SalpPositions(j,i)=FoodPosition(j)+c1*((ub(j)-lb(j))*c2+lb(j));
else
SalpPositions(j,i)=FoodPosition(j)-c1*((ub(j)-lb(j))*c2+lb(j));
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end
elseif i>N/2 && i<N+1
point1=SalpPositions(:,i-1);
point2=SalpPositions(:,i);
SalpPositions(:,i)=(point2+point1)/2; % % Eq. (3.4) in the paper
end
SalpPositions= SalpPositions';
end
for i=1:size(SalpPositions,1)
Tp=SalpPositions(i,:)>ub';Tm=SalpPositions(i,:)<lb';SalpPositions(i,:)=(SalpPositions(i,:).*(~(Tp+Tm)))+ub'.*Tp+lb'.*Tm;
SalpFitness(1,i)=fobj(SalpPositions(i,:));
if SalpFitness(1,i)<FoodFitness
FoodPosition=SalpPositions(i,:);
FoodFitness=SalpFitness(1,i);
end
end
Convergence_curve(l)=FoodFitness;
l = l + 1;
end

3 仿真结果

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_上传_08

【LSTM分类】基于麻雀算法优化LSTM实现数据分类含Matlab源码_d3_09编辑

4 参考文献

[1]陶晓玲, 王素芳, 赵峰,等. 基于麻雀搜索算法优化Bi-LSTM的网络安全态势预测方法:. 

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