www.gusucode.com > 《MATLAB智能算法30个案例》分析源代码 > 《MATLAB智能算法30个案例》分析源代码/code/chapter17/PSOt/pso_Trelea_vectorized.m
% 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
psi = ac1 + ac2;
chi_den = abs(2-psi-sqrt(psi^2 - 4*psi));
chi_num = 2*kappa;
chi = chi_num/chi_den;
end
end
% INITIALIZE END INITIALIZE END INITIALIZE END INITIALIZE END
rstflg = 0; % for dynamic environment checking
% start PSO iterative procedures
cnt = 0; % counter used for updating display according to df in the options
cnt2 = 0; % counter used for the stopping subroutine based on error convergence
iwt(1) = iw1;
for i=1:me % start epoch loop (iterations)
out = feval(functname,[pos;gbest]);
outbestval = out(end,:);
out = out(1:end-1,:);
tr(i+1) = gbestval; % keep track of global best val
te = i; % returns epoch number to calling program when done
bestpos(i,1:D+1) = [gbest,gbestval];
%assignin('base','bestpos',bestpos(i,1:D+1));
%------------------------------------------------------------------------
% this section does the plots during iterations
if plotflg==1
if (rem(i,df) == 0 ) | (i==me) | (i==1)
fprintf(message,i,gbestval);
cnt = cnt+1; % count how many times we display (useful for movies)
eval(plotfcn); % defined at top of script
end % end update display every df if statement
end % end plotflg if statement
% check for an error space that changes wrt time/iter
% threshold value that determines dynamic environment
% sees if the value of gbest changes more than some threshold value
% for the same location
chkdyn = 1;
rstflg = 0; % for dynamic environment checking
if chkdyn==1
threshld = 0.05; % percent current best is allowed to change, .05 = 5% etc
letiter = 5; % # of iterations before checking environment, leave at least 3 so PSO has time to converge
outorng = abs( 1- (outbestval/gbestval) ) >= threshld;
samepos = (max( sentry == gbest ));
if (outorng & samepos) & rem(i,letiter)==0
rstflg=1;
% disp('New Environment: reset pbest, gbest, and vel');
%% reset pbest and pbestval if warranted
% outpbestval = feval( functname,[pbest] );
% Poutorng = abs( 1-(outpbestval./pbestval) ) > threshld;
% pbestval = pbestval.*~Poutorng + outpbestval.*Poutorng;
% pbest = pbest.*repmat(~Poutorng,1,D) + pos.*repmat(Poutorng,1,D);
pbest = pos; % reset personal bests to current positions
pbestval = out;
vel = vel*10; % agitate particles a little (or a lot)
% recalculate best vals
if minmax == 1
[gbestval,idx1] = max(pbestval);
elseif minmax==0
[gbestval,idx1] = min(pbestval);
elseif minmax==2 % this section needs work
[temp,idx1] = min((pbestval-ones(size(pbestval))*errgoal).^2);
gbestval = pbestval(idx1);
end
gbest = pbest(idx1,:);
% 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
end % end if outorng
sentryval = gbestval;
sentry = gbest;
end % end if chkdyn
% find particles where we have new pbest, depending on minmax choice
% then find gbest and gbestval
%[size(out),size(pbestval)]
if rstflg == 0
if minmax == 0
[tempi] = find(pbestval>=out); % new min pbestvals
pbestval(tempi,1) = out(tempi); % update pbestvals
pbest(tempi,:) = pos(tempi,:); % update pbest positions
[iterbestval,idx1] = min(pbestval);
if gbestval >= iterbestval
gbestval = iterbestval;
gbest = pbest(idx1,:);
% 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
end
elseif minmax == 1
[tempi,dum] = find(pbestval<=out); % new max pbestvals
pbestval(tempi,1) = out(tempi,1); % update pbestvals
pbest(tempi,:) = pos(tempi,:); % update pbest positions
[iterbestval,idx1] = max(pbestval);
if gbestval <= iterbestval
gbestval = iterbestval;
gbest = pbest(idx1,:);
% 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
end
elseif minmax == 2 % this won't work as it is, fix it later
egones = errgoal*ones(ps,1); % vector of errgoals
sqrerr2 = ((pbestval-egones).^2);
sqrerr1 = ((out-egones).^2);
[tempi,dum] = find(sqerr1 <= sqrerr2); % find particles closest to targ
pbestval(tempi,1) = out(tempi,1); % update pbestvals
pbest(tempi,:) = pos(tempi,:); % update pbest positions
sqrerr = ((pbestval-egones).^2); % need to do this to reflect new pbests
[temp,idx1] = min(sqrerr);
iterbestval = pbestval(idx1);
if (iterbestval-errgoal)^2 <= (gbestval-errgoal)^2
gbestval = iterbestval;
gbest = pbest(idx1,:);
% 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
end
end
end
% % build a simple predictor 10th order, for gbest trajectory
% if i>500
% for dimcnt=1:D
% pred_coef = polyfit(i-250:i,(bestpos(i-250:i,dimcnt))',20);
% % pred_coef = polyfit(200:i,(bestpos(200:i,dimcnt))',20);
% gbest_pred(i,dimcnt) = polyval(pred_coef,i+1);
% end
% else
% gbest_pred(i,:) = zeros(size(gbest));
% end
%gbest_pred(i,:)=gbest;
%assignin('base','gbest_pred',gbest_pred);
% % convert to non-inertial frame
% gbestoffset = gbest - gbest_pred(i,:);
% gbest = gbest - gbestoffset;
% pos = pos + repmat(gbestoffset,ps,1);
% pbest = pbest + repmat(gbestoffset,ps,1);
%PSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSO
% get new velocities, positions (this is the heart of the PSO algorithm)
% each epoch get new set of random numbers
rannum1 = rand([ps,D]); % for Trelea and Clerc types
rannum2 = rand([ps,D]);
if trelea == 2
% from Trelea's paper, parameter set 2
vel = 0.729.*vel... % prev vel
+1.494.*rannum1.*(pbest-pos)... % independent
+1.494.*rannum2.*(repmat(gbest,ps,1)-pos); % social
elseif trelea == 1
% from Trelea's paper, parameter set 1
vel = 0.600.*vel... % prev vel
+1.700.*rannum1.*(pbest-pos)... % independent
+1.700.*rannum2.*(repmat(gbest,ps,1)-pos); % social
elseif trelea ==3
% Clerc's Type 1" PSO
vel = chi*(vel... % prev vel
+ac1.*rannum1.*(pbest-pos)... % independent
+ac2.*rannum2.*(repmat(gbest,ps,1)-pos)) ; % social
else
% common PSO algo with inertia wt
% get inertia weight, just a linear funct w.r.t. epoch parameter iwe
if i<=iwe
iwt(i) = ((iw2-iw1)/(iwe-1))*(i-1)+iw1;
else
iwt(i) = iw2;
end
% random number including acceleration constants
ac11 = rannum1.*ac1; % for common PSO w/inertia
ac22 = rannum2.*ac2;
vel = iwt(i).*vel... % prev vel
+ac11.*(pbest-pos)... % independent
+ac22.*(repmat(gbest,ps,1)-pos); % social
end
% limit velocities here using masking
vel = ( (vel <= velmaskmin).*velmaskmin ) + ( (vel > velmaskmin).*vel );
vel = ( (vel >= velmaskmax).*velmaskmax ) + ( (vel < velmaskmax).*vel );
% update new position (PSO algo)
pos = pos + vel;
% position masking, limits positions to desired search space
% method: 0) no position limiting, 1) saturation at limit,
% 2) wraparound at limit , 3) bounce off limit
minposmask_throwaway = pos <= posmaskmin; % these are psXD matrices
minposmask_keep = pos > posmaskmin;
maxposmask_throwaway = pos >= posmaskmax;
maxposmask_keep = pos < posmaskmax;
if posmaskmeth == 1
% this is the saturation method
pos = ( minposmask_throwaway.*posmaskmin ) + ( minposmask_keep.*pos );
pos = ( maxposmask_throwaway.*posmaskmax ) + ( maxposmask_keep.*pos );
elseif posmaskmeth == 2
% this is the wraparound method
pos = ( minposmask_throwaway.*posmaskmax ) + ( minposmask_keep.*pos );
pos = ( maxposmask_throwaway.*posmaskmin ) + ( maxposmask_keep.*pos );
elseif posmaskmeth == 3
% this is the bounce method, particles bounce off the boundaries with -vel
pos = ( minposmask_throwaway.*posmaskmin ) + ( minposmask_keep.*pos );
pos = ( maxposmask_throwaway.*posmaskmax ) + ( maxposmask_keep.*pos );
vel = (vel.*minposmask_keep) + (-vel.*minposmask_throwaway);
vel = (vel.*maxposmask_keep) + (-vel.*maxposmask_throwaway);
else
% no change, this is the original Eberhart, Kennedy method,
% it lets the particles grow beyond bounds if psoparams (P)
% especially Vmax, aren't set correctly, see the literature
end
%PSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSOPSO
% check for stopping criterion based on speed of convergence to desired
% error
tmp1 = abs(tr(i) - gbestval);
if tmp1 > ergrd
cnt2 = 0;
elseif tmp1 <= ergrd
cnt2 = cnt2+1;
if cnt2 >= ergrdep
if plotflg == 1
fprintf(message,i,gbestval);
disp(' ');
disp(['--> Solution likely, GBest hasn''t changed by at least ',...
num2str(ergrd),' for ',...
num2str(cnt2),' epochs.']);
eval(plotfcn);
end
break
end
end
% this stops if using constrained optimization and goal is reached
if ~isnan(errgoal)
if ((gbestval<=errgoal) & (minmax==0)) | ((gbestval>=errgoal) & (minmax==1))
if plotflg == 1
fprintf(message,i,gbestval);
disp(' ');
disp(['--> Error Goal reached, successful termination!']);
eval(plotfcn);
end
break
end
% this is stopping criterion for constrained from both sides
if minmax == 2
if ((tr(i)<errgoal) & (gbestval>=errgoal)) | ((tr(i)>errgoal) ...
& (gbestval <= errgoal))
if plotflg == 1
fprintf(message,i,gbestval);
disp(' ');
disp(['--> Error Goal reached, successful termination!']);
eval(plotfcn);
end
break
end
end % end if minmax==2
end % end ~isnan if
% % convert back to inertial frame
% pos = pos - repmat(gbestoffset,ps,1);
% pbest = pbest - repmat(gbestoffset,ps,1);
% gbest = gbest + gbestoffset;
end % end epoch loop
%% clear temp outputs
% evalin('base','clear temp_pso_out temp_te temp_tr;');
% output & return
OUT=[gbest';gbestval];
varargout{1}=[1:te];
varargout{2}=[tr(find(~isnan(tr)))];
return