【樽海鞘算法】基于多子群的共生非均匀高斯变异樽海鞘群算法求解单目标优化问题附matlab代码MSNSSA
【樽海鞘算法】基于多子群的共生非均匀高斯变异樽海鞘群算法求解单目标优化问题附matlab代码MSNSSA
TT_Matlab
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1 简介
针对樽海鞘群算法求解精度不高和收敛速度慢等缺点, 提出一种多子群的共生非均匀高斯变异樽海鞘群算法(Multi-subpopulation based symbiosis and non-uniform Gaussian mutation salp swarm algorithm, MSNSSA). 根据不同适应度值将樽海鞘链群分为三个子种群, 各个子种群分别进行领导者位置更新,追随者共生策略和链尾者非均匀高斯变异等操作. 使用统计分析,收敛速度分析,Wilcoxon检验,经典基准函数和CEC 2014函数的标准差来评估改进樽海鞘群算法的效率. 结果表明, 改进算法具有更好的寻优精度和收敛速度. 尤其在求解高维和多峰测试函数上, 改进算法拥有更好性能.
2 部分代码
%%
樽海鞘算法
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);
Convergence_curve(1)
=
inf;
%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
4 参考文献
[1]陈忠云, 张达敏, 辛梓芸. 多子群的共生非均匀高斯变异樽海鞘群算法[J]. 自动化学报, 2022, 48(5):1307-1317.
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。