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# 鸡群算法

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 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    % Update the cNum chicks' values.         x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) + ( pX( ...             sortIndex( mother( i - rNum - hNum ) ), : ) - ...             pX( sortIndex( i ), : ) ) .* FL( i );         x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub );         fit( sortIndex( i ) ) = FitFunc( x( sortIndex( i ), : ) );     end         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%    % Update the individual's best fitness vlaue and the global best one         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 end % End of the main program %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % The following functions are associated with the main program %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % This function is the objective function function y = Sphere( x ) y = sum( x .^ 2 ); % 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; %-------------------------------------------------------------------------- % This function generate "dim" values, all of which are different from %  the value of "tabu" function value = randiTabu( min, max, tabu, dim ) value = ones( dim, 1 ) .* max .* 2; num = 1; while ( num <= dim )     temp = randi( [min, max], 1, 1 );     if( length( find( value ~= temp ) ) == dim && temp ~= tabu )         value( num ) = temp;         num = num + 1;     end end %-------------------------------------------------------------------------- function result = randpermF( range, dim ) % The original function "randperm" in Matlab is only confined to the % situation that dimension is no bigger than dim. This function is % applied to solve that situation. temp = randperm( range, range ); temp2 = randi( range, dim, 1 ); index = randperm( dim, ( dim - range ) ); result = [ temp, temp2( index )' ];复制代码代码下载：https://pan.baidu.com/s/18So1iW4q2ifcMruSuksqEg

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