pso_trelea_vectorized.m

本文是关于粒子群算法(pso)的介绍和相关MATLAB源程序,是智能天线权值优化方面的一种很前沿的算法

% pso_Trelea_vectorized.m
% a generic particle swarm optimizer
% to find the minimum or maximum of any 
% MISO matlab function
%
% Implements Common, Trelea type 1 and 2, and Clerc's class 1". It will
% also automatically try to track to a changing environment (with varied
% success - BKB 3/18/05)
%
% This vectorized version removes the for loop associated with particle
% number. It also *requires* that the cost function have a single input
% that represents all dimensions of search (i.e., for a function that has 2
% inputs then make a wrapper that passes a matrix of ps x 2 as a single
% variable)
%
% Usage:
%  [optOUT]=PSO(functname,D)
% or:
%  [optOUT,tr,te]=...
%        PSO(functname,D,mv,VarRange,minmax,PSOparams,plotfcn,PSOseedValue)
%
% Inputs:
%    functname - string of matlab function to optimize
%    D - # of inputs to the function (dimension of problem)
%    
% Optional Inputs:
%    mv - max particle velocity, either a scalar or a vector of length D
%           (this allows each component to have it's own max velocity), 
%           default = 4, set if not input or input as NaN
%
%    VarRange - matrix of ranges for each input variable, 
%      default -100 to 100, of form:
%       [ min1 max1 
%         min2 max2
%            ...
%         minD maxD ]
%
%    minmax = 0, funct minimized (default)
%           = 1, funct maximized
%           = 2, funct is targeted to P(12) (minimizes distance to errgoal)
%
%    PSOparams - PSO parameters
%      P(1) - Epochs between updating display, default = 100. if 0, 
%             no display
%      P(2) - Maximum number of iterations (epochs) to train, default = 2000.
%      P(3) - population size, default = 24
%
%      P(4) - acceleration const 1 (local best influence), default = 2
%      P(5) - acceleration const 2 (global best influence), default = 2
%      P(6) - Initial inertia weight, default = 0.9
%      P(7) - Final inertia weight, default = 0.4
%      P(8) - Epoch when inertial weight at final value, default = 1500
%      P(9)- minimum global error gradient, 
%                 if abs(Gbest(i+1)-Gbest(i)) < gradient over 
%                 certain length of epochs, terminate run, default = 1e-25
%      P(10)- epochs before error gradient criterion terminates run, 
%                 default = 150, if the SSE does not change over 250 epochs
%                               then exit
%      P(11)- error goal, if NaN then unconstrained min or max, default=NaN
%      P(12)- type flag (which kind of PSO to use)
%                 0 = Common PSO w/intertia (default)
%                 1,2 = Trelea types 1,2
%                 3   = Clerc's Constricted PSO, Type 1"
%      P(13)- PSOseed, default=0
%               = 0 for initial positions all random
%               = 1 for initial particles as user input
%
%    plotfcn - optional name of plotting function, default 'goplotpso',
%              make your own and put here
%
%    PSOseedValue - initial particle position, depends on P(13), must be
%                   set if P(13) is 1 or 2, not used for P(13)=0, needs to
%                   be nXm where n<=ps, and m<=D
%                   If n<ps and/or m<D then remaining values are set random
%                   on Varrange
% Outputs:
%    optOUT - optimal inputs and associated min/max output of function, of form:
%        [ bestin1
%          bestin2
%            ...
%          bestinD
%          bestOUT ]
%
% Optional Outputs:
%    tr    - Gbest at every iteration, traces flight of swarm
%    te    - epochs to train, returned as a vector 1:endepoch
%
% Example:  out=pso_Trelea_vectorized('f6',2)

% Brian Birge
% Rev 3.3
% 2/18/06

function [OUT,varargout]=pso_Trelea_vectorized(functname,D,varargin)

rand('state',sum(100*clock));
if nargin < 2
   error('Not enough arguments.');
end

% PSO PARAMETERS
if nargin == 2      % only specified functname and D
   VRmin=ones(D,1)*-100; 
   VRmax=ones(D,1)*100;    
   VR=[VRmin,VRmax];
   minmax = 0;
   P = [];
   mv = 4;
   plotfcn='goplotpso';   
elseif nargin == 3  % specified functname, D, and mv
   VRmin=ones(D,1)*-100; 
   VRmax=ones(D,1)*100;    
   VR=[VRmin,VRmax];
   minmax = 0;
   mv=varargin{1};
   if isnan(mv)
       mv=4;
   end
   P = [];
   plotfcn='goplotpso';   
elseif nargin == 4  % specified functname, D, mv, Varrange
   mv=varargin{1};
   if isnan(mv)
       mv=4;
   end
   VR=varargin{2}; 
   minmax = 0;
   P = [];
   plotfcn='goplotpso';   
elseif nargin == 5  % Functname, D, mv, Varrange, and minmax
   mv=varargin{1};
   if isnan(mv)
       mv=4;
   end    
   VR=varargin{2};
   minmax=varargin{3};
   P = [];
   plotfcn='goplotpso';
elseif nargin == 6  % Functname, D, mv, Varrange, minmax, and psoparams
   mv=varargin{1};
   if isnan(mv)
       mv=4;
   end    
   VR=varargin{2};
   minmax=varargin{3};
   P = varargin{4}; % psoparams
   plotfcn='goplotpso';   
elseif nargin == 7  % Functname, D, mv, Varrange, minmax, and psoparams, plotfcn
   mv=varargin{1};
   if isnan(mv)
       mv=4;
   end    
   VR=varargin{2};
   minmax=varargin{3};
   P = varargin{4}; % psoparams
   plotfcn = varargin{5}; 
elseif nargin == 8  % Functname, D, mv, Varrange, minmax, and psoparams, plotfcn, PSOseedValue
   mv=varargin{1};
   if isnan(mv)
       mv=4;
   end    
   VR=varargin{2};
   minmax=varargin{3};
   P = varargin{4}; % psoparams
   plotfcn = varargin{5};  
   PSOseedValue = varargin{6};
else    
   error('Wrong # of input arguments.');
end

% sets up default pso params
Pdef = [100 2000 24 2 2 0.9 0.4 1500 1e-25 250 NaN 0 0];
Plen = length(P);
P    = [P,Pdef(Plen+1:end)];

df      = P(1);
me      = P(2);
ps      = P(3);
ac1     = P(4);
ac2     = P(5);
iw1     = P(6);
iw2     = P(7);
iwe     = P(8);
ergrd   = P(9);
ergrdep = P(10);
errgoal = P(11);
trelea  = P(12);
PSOseed = P(13);

% used with trainpso, for neural net training
if strcmp(functname,'pso_neteval')
   net = evalin('caller','net');
    Pd = evalin('caller','Pd');
    Tl = evalin('caller','Tl');
    Ai = evalin('caller','Ai');
     Q = evalin('caller','Q');
    TS = evalin('caller','TS');
end


% error checking
 if ((minmax==2) & isnan(errgoal))
     error('minmax= 2, errgoal= NaN: choose an error goal or set minmax to 0 or 1');
 end

 if ( (PSOseed==1) & ~exist('PSOseedValue') )
     error('PSOseed flag set but no PSOseedValue was input');
 end

 if exist('PSOseedValue')
     tmpsz=size(PSOseedValue);
     if D < tmpsz(2)
         error('PSOseedValue column size must be D or less');
     end
     if ps < tmpsz(1)
         error('PSOseedValue row length must be # of particles or less');
     end
 end
 
% set plotting flag
if (P(1))~=0
  plotflg=1;
else
  plotflg=0;
end

% preallocate variables for speed up
 tr = ones(1,me)*NaN;

% take care of setting max velocity and position params here
if length(mv)==1
 velmaskmin = -mv*ones(ps,D);     % min vel, psXD matrix
 velmaskmax = mv*ones(ps,D);      % max vel
elseif length(mv)==D     
 velmaskmin = repmat(forcerow(-mv),ps,1); % min vel
 velmaskmax = repmat(forcerow( mv),ps,1); % max vel
else
 error('Max vel must be either a scalar or same length as prob dimension D');
end
posmaskmin  = repmat(VR(1:D,1)',ps,1);  % min pos, psXD matrix
posmaskmax  = repmat(VR(1:D,2)',ps,1);  % max pos
posmaskmeth = 3; % 3=bounce method (see comments below inside epoch loop)

% PLOTTING
 message = sprintf('PSO: %%g/%g iterations, GBest = %%20.20g.\n',me);
 
% INITIALIZE INITIALIZE INITIALIZE INITIALIZE INITIALIZE INITIALIZE
 
% initialize population of particles and their velocities at time zero,
% format of pos= (particle#, dimension)
 % construct random population positions bounded by VR
  pos(1:ps,1:D) = normmat(rand([ps,D]),VR',1);
  
  if PSOseed == 1         % initial positions user input, see comments above
    tmpsz                      = size(PSOseedValue);
    pos(1:tmpsz(1),1:tmpsz(2)) = PSOseedValue;  
  end

 % construct initial random velocities between -mv,mv
  vel(1:ps,1:D) = normmat(rand([ps,D]),...
      [forcecol(-mv),forcecol(mv)]',1);

% initial pbest positions vals
 pbest = pos;

% VECTORIZE THIS, or at least vectorize cost funct call 
 out = feval(functname,pos);  % returns column of cost values (1 for each particle)
%---------------------------
 
 pbestval=out;   % initially, pbest is same as pos

% assign initial gbest here also (gbest and gbestval)
 if minmax==1
   % this picks gbestval when we want to maximize the function
    [gbestval,idx1] = max(pbestval);
 elseif minmax==0
   % this works for straight minimization
    [gbestval,idx1] = min(pbestval);
 elseif minmax==2
   % this works when you know target but not direction you need to go
   % good for a cost function that returns distance to target that can be either
   % negative or positive (direction info)
    [temp,idx1] = min((pbestval-ones(size(pbestval))*errgoal).^2);
    gbestval    = pbestval(idx1);
 end

 % preallocate a variable to keep track of gbest for all iters
 bestpos        = zeros(me,D+1)*NaN;
 gbest          = pbest(idx1,:);  % this is gbest position
   % used with trainpso, for neural net training
   % assign gbest to net at each iteration, these interim assignments
   % are for plotting mostly
    if strcmp(functname,'pso_neteval')
        net=setx(net,gbest);
    end
 %tr(1)          = gbestval;       % save for output
 bestpos(1,1:D) = gbest;
 
% this part used for implementing Carlisle and Dozier's APSO idea
% slightly modified, this tracks the global best as the sentry whereas
% their's chooses a different point to act as sentry
% see "Tracking Changing Extremea with Adaptive Particle Swarm Optimizer",
% part of the WAC 2002 Proceedings, June 9-13, http://wacong.com
 sentryval = gbestval;
 sentry    = gbest;
 
if (trelea == 3)
% calculate Clerc's constriction coefficient chi to use in his form
 kappa   = 1; % standard val = 1, change for more or less constriction    
 if ( (ac1+ac2) <=4 )
     chi = kappa;
 else