Mercurial > forge
changeset 6685:81e660f66ce0 octave-forge
de_min: minor ch changes, cleanup
| author | sffisch |
|---|---|
| date | Thu, 11 Feb 2010 13:50:39 +0000 |
| parents | b0e6ff889eb3 |
| children | 200d614223e2 |
| files | main/optim/inst/de_min.m |
| diffstat | 1 files changed, 21 insertions(+), 32 deletions(-) [+] |
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--- a/main/optim/inst/de_min.m Thu Feb 11 11:18:43 2010 +0000 +++ b/main/optim/inst/de_min.m Thu Feb 11 13:50:39 2010 +0000 @@ -1,4 +1,4 @@ -## Copyright (C) 2009 Christian Fischer <fischer@itm.uni-stuttgart.de> +## Copyright (C) 2009 Christian Fischer <cfischer@itm.uni-stuttgart.de> ## Copyright (C) 1996, 1997 R. Storn ## ## This program is free software; you can redistribute it and/or modify @@ -25,6 +25,7 @@ ## minimization of a user-supplied function with respect to x(1:D), ## using the differential evolution (DE) method based on an algorithm ## by Rainer Storn (http://www.icsi.berkeley.edu/~storn/code.html) +## See: http://www.softcomputing.net/tevc2009_1.pdf ## ## ## Arguments: @@ -120,7 +121,7 @@ ## ctl.XVmax = [ 2 2]; ## [x, obj_value, nfeval, convergence] = de_min (@f, ctl); ## -## Author : Christian Fischer <fischer@itm.uni-stuttgart.de> +## Author : Christian Fischer <cfischer@itm.uni-stuttgart.de> ## Keywords: global-optimisation optimisation minimisation function [bestmem, bestval, nfeval, convergence] = de_min(fcn, varargin) @@ -256,26 +257,11 @@ ## popold is the population which has to compete. It is static ## through one iteration. pop is the newly emerging population. -pm1 = zeros (NP, D); # initialize population matrix 1 -pm2 = zeros (NP, D); # initialize population matrix 2 -pm3 = zeros (NP, D); # initialize population matrix 3 -pm4 = zeros (NP, D); # initialize population matrix 4 -pm5 = zeros (NP, D); # initialize population matrix 5 -bm = zeros (NP, D); # initialize bestmember matrix bm_n= zeros (NP, D); # initialize bestmember matrix in neighbourh. -ui = zeros (NP, D); # intermediate population of perturbed vectors -mui = zeros (NP, D); # mask for intermediate population -mpo = zeros (NP, D); # mask for old population +lpm1= zeros (NP, D); # initialize local population matrix 1 +lpm1= zeros (NP, D); # initialize local population matrix 2 rot = 0:1:NP-1; # rotating index array (size NP) rotd= 0:1:D-1; # rotating index array (size D) -rt = zeros (NP); # another rotating index array -rtd = zeros (D); # rotating index array for exponential crossover -a1 = zeros (NP); # index array -a2 = zeros (NP); # index array -a3 = zeros (NP); # index array -a4 = zeros (NP); # index array -a5 = zeros (NP); # index array -ind = zeros (4); iter = 1; while ((iter < maxiter) & (nfeval < maxnfe) & (bestval > VTR) & ... @@ -298,16 +284,15 @@ pm1 = popold(a1,:); # shuffled population 1 pm2 = popold(a2,:); # shuffled population 2 pm3 = popold(a3,:); # shuffled population 3 - pm4 = popold(a4,:); # shuffled population 4 - pm5 = popold(a5,:); # shuffled population 5 w1 = wold(a4); # shuffled weightings 1 w2 = wold(a5); # shuffled weightings 2 bm = repmat (bestmemit, NP, 1); # population filled with the best member # of the last iteration - bw = repmat(bestw, NP, 1); # the same for the weighting of the best + bw = repmat (bestw, NP, 1); # the same for the weighting of the best - mui = rand (NP, D) < CR; # all random numbers < CR are 1, 0 otherwise + mui = rand (NP, D) < CR; # mask for intermediate population + # all random numbers < CR are 1, 0 otherwise if (strategy > 6) st = strategy - 6; # binomial crossover @@ -332,14 +317,18 @@ elseif (st == 3) # DE/target-to-best/1 ui = popold + F*(bm-popold) + F*(pm1 - pm2); elseif (st == 4) # DE/best/2 + pm4 = popold(a4,:); # shuffled population 4 + pm5 = popold(a5,:); # shuffled population 5 ui = bm + F*(pm1 - pm2 + pm3 - pm4); # differential variation elseif (st == 5) # DE/rand/2 + pm4 = popold(a4,:); # shuffled population 4 + pm5 = popold(a5,:); # shuffled population 5 ui = pm5 + F*(pm1 - pm2 + pm3 - pm4); # differential variation else # DEGL/SAW ## The DEGL/SAW method is more complicated. ## We have to generate a neighbourhood first. - ## The neighbourhood size is 10% of NP and at least 3. - nr = max (1, floor ((0.1*NP -1)/2)); # neighbourhood radius + ## The neighbourhood size is 10% of NP and at least 1. + nr = max (1, ceil ((0.1*NP -1)/2)); # neighbourhood radius ## FIXME: I don't know how to vectorise this. - if possible for i = 1:NP neigh_ind = i-nr:i+nr; # index range of neighbourhood @@ -349,19 +338,19 @@ bm_n(i,:) = popold(neigh_ind(ix),:); # copy the data from the local best neigh_ind(ix) = []; # remove "i" pq = neigh_ind(randperm (length (neigh_ind))); - # permutation of the remaining ind. - pm4(i,:) = popold(pq(1),:); # use the pm4/5 matrix for the random - pm5(i,:) = popold(pq(2),:); # point p,q + # permutation of the remaining ind. + lpm1(i,:) = popold(pq(1),:); # create the local pop member matrix + lpm2(i,:) = popold(pq(2),:); # for the random point p,q endfor ## calculate the new weihting factors wi = wold + F*(bw - wold) + F*(w1 - w2); # use DE/target-to-best/1/nocross # for optimisation of weightings ## fix bounds for weightings - o = ones (NP, 1); + o = ones (NP, 1); wi = sort ([0.05*o, wi, 0.95*o],2)(:,2); # sort and take the second column ## fill weighting matrix wm = repmat (wi, 1, D); - li = popold + F*(bm_n- popold) + F*(pm4 - pm5); + li = popold + F*(bm_n- popold) + F*(lpm1 - lpm2); gi = popold + F*(bm - popold) + F*(pm1 - pm2); ui = wm.*gi + (1-wm).*li; # combine global and local part endif @@ -385,7 +374,7 @@ w(i) = wi(i); # save the weighting factor ## we update bestval only in case of success to save time - if (tempval < bestval) # if competitor better than the best one ever + if (tempval <= bestval) # if competitor better than the best one ever bestval = tempval; # new best value bestmem = ui(i,:); # new best parameter vector ever bestw = wi(i); # save best weighting @@ -426,7 +415,7 @@ end end -## create the convergence output +## create the convergence result if (bestval <= VTR) convergence = 1; elseif (abs (max (val) - bestval) / max (1, abs (max (val))) <= tol)