CS_O.zip_图像处理应用_鸡群算法

% ------------------------------------------------------------------------- % Chicken Swarm Optimization (CSO) (demo) % Programmed by Xian-Bing Meng % Updated at Jun 21, 2015. % Email: x.b.meng12@gmail.com % % This is a simple demo version only implemented the basic idea of CSO for % solving the unconstrained problem, namely Sphere function. % The details about CSO are illustratred in the following paper. % Xian-Bing Meng, et al. A new bio-inspired algorithm: Chicken Swarm % Optimization. The Fifth International Conference on Swarm Intelligence % % The parameters in CSO are presented as follows. % FitFunc % The objective function % M % Maxmimal generations (iterations) % pop % Population size % dim % Dimension % G % How often the chicken swarm can be updated. % rPercent % The population size of roosters accounts for "rPercent" % percent of the total population size % hPercent % The population size of hens accounts for "hPercent" percent % of the total population size % mPercent % The population size of mother hens accounts for "mPercent" % percent of the population size of hens % % Using the default value,CSO can be executed using the following code. % [ bestX, fMin ] = CSO % ------------------------------------------------------------------------- %************************************************************************* % Revision 1 % Revised at May 23, 2015 % 1.Note that the previous version of CSO doen't consider the situation % that there maybe not exist hens in a group. % We assume there exist at least one hen in each group. % Revision 2 % Revised at Jun 24, 2015 % 1.Correct an error at line "100". %************************************************************************* % Main programs function [ bestX, fMin ] = CSO( FitFunc, M, pop, dim, G, rPercent, hPercent, mPercent ) % Display help help CSO.m %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % set the default parameters if nargin < 1 FitFunc = @Sphere; M = 1000; pop = 100; dim = 20; G = 10; rPercent = 0.15; hPercent = 0.7; mPercent = 0.5; end rNum = round( pop * rPercent ); % The population size of roosters hNum = round( pop * hPercent ); % The population size of hens cNum = pop - rNum - hNum; % The population size of chicks mNum = round( hNum * mPercent ); % The population size of mother hens lb= -100*ones( 1,dim ); % Lower bounds ub= 100*ones( 1,dim ); % Upper bounds %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Initialization for i = 1 : pop x( i, : ) = lb + (ub - lb) .* rand( 1, dim ); fit( i ) = FitFunc( x( i, : ) ); end pFit = fit; % The individual's best fitness value pX = x; % The individual's best position corresponding to the pFit [ fMin, bestIndex ] = min( fit ); % fMin denotes the global optimum % bestX denotes the position corresponding to fMin bestX = x( bestIndex, : ); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Start the iteration. for t = 1 : M % This parameter is to describe how closely the chicks would follow % their mother to forage for food. In fact, there exist cNum chicks, % thus only cNum values of FL would be used. FL = rand( pop, 1 ) .* 0.4 + 0.5; % The chicken swarm'status about hierarchal order, dominance % relationship, mother-child relationship, the roosters, hens and the % chicks in a group will remain stable during a period of time. These % statuses can be updated every several (G) time steps.The parameter G % is used to simulate the situation that the chicken swarm have been % changed, including some chickens have died, or the chicks have grown % up and became roosters or hens, some mother hens have hatched new % offspring (chicks) and so on. if mod( t, G ) == 1 || t == 1 [ ans, sortIndex ] = sort( pFit ); % How the chicken swarm can be divided into groups and the identity % of the chickens (roosters, hens and chicks) can be determined all % depend on the fitness values of the chickens themselves. Hence we % use sortIndex(i) to describe the chicken, not the index i itself. motherLib = randperm( hNum, mNum ) + rNum; % Randomly select mNum hens which would be the mother hens. % We assume that all roosters are stronger than the hens, likewise, % hens are stronger than the chicks.In CSO, the strong is reflected % by the good fitness value. Here, the optimization problems is % minimal ones, thus the more strong ones correspond to the ones % with lower fitness values. % Given the fact the 1 : rNum chickens' fitness values maybe not % the best rNum ones. Thus we use sortIndex( 1 : rNum ) to describe % the roosters. In turn, sortIndex( (rNum + 1) :(rNum + 1 + hNum )) % to describle the mother hens, .....chicks. % Here motherLib include all the mother hens. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Randomly select each hen's mate, rooster. Hence we can determine % which group each hen inhabits using "mate".Each rooster stands % for a group.For simplicity, we assume that there exist only one % rooster and at least one hen in each group. mate = randpermF( rNum, hNum ); % Randomly select cNum chicks' mother hens mother = motherLib( randi( mNum, cNum, 1 ) ); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i = 1 : rNum % Update the rNum roosters' values. % randomly select another rooster different from the i (th) one. anotherRooster = randiTabu( 1, rNum, i, 1 ); if( pFit( sortIndex( i ) ) <= pFit( sortIndex( anotherRooster ) ) ) tempSigma = 1; else tempSigma = exp( ( pFit( sortIndex( anotherRooster ) ) - ... pFit( sortIndex( i ) ) ) / ( abs( pFit( sortIndex(i) ) )... + realmin ) ); end x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) .* ( 1 + ... tempSigma .* randn( 1, dim ) ); x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = FitFunc( x( sortIndex( i ), : ) ); end for i = ( rNum + 1 ) : ( rNum + hNum ) % Update the hNum hens' values. other = randiTabu( 1, i, mate( i - rNum ), 1 ); % randomly select another chicken different from the i (th) % chicken's mate. Note that the "other" chicken's fitness value % should be superior to that of the i (th) chicken. This means the % i (th) chicken may steal the better food found by the "other" % (th) chicken. c1 = exp( ( pFit( sortIndex( i ) ) - pFit( sortIndex( mate( i - ... rNum ) ) ) )/ ( abs( pFit( sortIndex(i) ) ) + realmin ) ); c2 = exp( ( -pFit( sortIndex( i ) ) + pFit( sortIndex( other ) ))); x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) + ( pX(... sortIndex( mate( i - rNum ) ), : )- pX( sortIndex( i ), : ) )... .* c1 .* rand( 1, dim ) + ( pX( sortIndex( other ), : ) - ... pX( sortIndex( i ), : ) ) .* c2 .* rand( 1, dim ); x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = FitFunc( x( sortIndex( i ), : ) ); end for i = ( rNum + hNum + 1 ) : pop % Updat