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⛄ 内容介绍
针对元启发算法中麻雀搜索算法(SSA)的早熟收敛,易陷入局部最优,全局搜索性差等问题进行研究,提出一种自适应螺旋飞行麻雀搜索算法(ASFSSA).
⛄ 部分代码
function [fMin , bestX,Convergence_curve ] = SSA(pop, M,c,d,dim,fobj )
P_percent = 0.2; % The population size of producers accounts for "P_percent" percent of the total population size
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
pNum = round( pop * P_percent ); % The population size of the producers
lb= c.*ones( 1,dim ); % Lower limit/bounds/ a vector
ub= d.*ones( 1,dim ); % Upper limit/bounds/ a vector
%Initialization
for i = 1 : pop
x( i, : ) = lb + (ub - lb) .* rand( 1, dim );
fit( i ) = fobj( x( i, : ) ) ;
end
pFit = fit;
pX = x; % The individual's best position corresponding to the pFit
[ fMin, bestI ] = min( fit ); % fMin denotes the global optimum fitness value
bestX = x( bestI, : ); % bestX denotes the global optimum position corresponding to fMin
% Start updating the solutions.
for t = 1 : M
[ ans, sortIndex ] = sort( pFit );% Sort.
[fmax,B]=max( pFit );
worse= x(B,:);
r2=rand(1);
if(r2<0.8)
for i = 1 : pNum % Equation (3)
r1=rand(1);
x( sortIndex( i ), : ) = pX( sortIndex( i ), : )*exp(-(i)/(r1*M));
x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
fit( sortIndex( i ) ) = fobj( x( sortIndex( i ), : ) );
end
else
for i = 1 : pNum
x( sortIndex( i ), : ) = pX( sortIndex( i ), : )+randn(1)*ones(1,dim);
x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
fit( sortIndex( i ) ) = fobj( x( sortIndex( i ), : ) );
end
end
[ fMMin, bestII ] = min( fit );
bestXX = x( bestII, : );
for i = ( pNum + 1 ) : pop % Equation (4)
A=floor(rand(1,dim)*2)*2-1;
if( i>(pop/2))
x( sortIndex(i ), : )=randn(1)*exp((worse-pX( sortIndex( i ), : ))/(i)^2);
else
x( sortIndex( i ), : )=bestXX+(abs(( pX( sortIndex( i ), : )-bestXX)))*(A'*(A*A')^(-1))*ones(1,dim);
end
x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );
fit( sortIndex( i ) ) = fobj( x( sortIndex( i ), : ) );
end
c=randperm(numel(sortIndex));
b=sortIndex(c(1:20));
for j = 1 : length(b) % Equation (5)
if( pFit( sortIndex( b(j) ) )>(fMin) )
x( sortIndex( b(j) ), : )=bestX+(randn(1,dim)).*(abs(( pX( sortIndex( b(j) ), : ) -bestX)));
else
x( sortIndex( b(j) ), : ) =pX( sortIndex( b(j) ), : )+(2*rand(1)-1)*(abs(pX( sortIndex( b(j) ), : )-worse))/ ( pFit( sortIndex( b(j) ) )-fmax+1e-50);
end
x( sortIndex(b(j) ), : ) = Bounds( x( sortIndex(b(j) ), : ), lb, ub );
fit( sortIndex( b(j) ) ) = fobj( x( sortIndex( b(j) ), : ) );
end
for i = 1 : pop
if ( fit( i ) < pFit( i ) )
pFit( i ) = fit( i );
pX( i, : ) = x( i, : );
end
if( pFit( i ) < fMin )
fMin= pFit( i );
bestX = pX( i, : );
end
end
Convergence_curve(t)=fMin;
end
% Application of simple limits/bounds
function s = Bounds( s, Lb, Ub)
% Apply the lower bound vector
temp = s;
I = temp < Lb;
temp(I) = Lb(I);
% Apply the upper bound vector
J = temp > Ub;
temp(J) = Ub(J);
% Update this new move
s = temp;
%---------------------------------------------------------------------------------------------------------------------------
⛄ 运行结果
⛄ 参考文献
[1] 刘睿莫愿斌. 动态优化问题的瞬态自适应麻雀搜索算法求解[J]. 计算机应用研究, 2022, 39(12):3651-3657.
[2] 高晨峰, 陈家清, 石默涵. 融合黄金正弦和曲线自适应的多策略麻雀搜索算法[J]. 计算机应用研究, 2022, 39(2):9.
[3] 周玉, 房倩, 裴泽宣,等. 基于切线飞行的麻雀搜索算法[J]. 计算机应用研究, 2023, 40(1):6.
