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agomez |
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#include "gadget.h" |
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#include "optinfo.h" |
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#include "mathfunc.h" |
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#include "doublevector.h" |
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#include "intvector.h" |
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#include "errorhandler.h" |
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#include "ecosystem.h" |
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#include "global.h" |
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#include "pso.h" |
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ulcessvp |
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#include <float.h>
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agomez |
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#ifdef _OPENMP
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#include "omp.h"
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#endif
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extern Ecosystem* EcoSystem;
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#ifdef _OPENMP
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extern Ecosystem** EcoSystems;
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#endif
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//==============================================================
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//// calulate swarm size based on dimensionality
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int OptInfoPso::pso_calc_swarm_size(int dim) {
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int size = 10. + 2. * sqrt(dim);
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ulcessvp |
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return (size > PSO_MAX_SIZE ? PSO_MAX_SIZE : size);
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}
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agomez |
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void OptInfoPso::OptimiseLikelihood() {
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ulcessvp |
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handle.logMessage(LOGINFO, "\nStarting PSO optimisation algorithm\n");
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double bestf, tmp;
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int i, offset, rchange, rcheck, rnumber;
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int nvars = EcoSystem->numOptVariables();
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DoubleVector x(nvars);
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// DoubleVector trialx(nvars);
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DoubleVector bestx(nvars);
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DoubleVector lowerb(nvars);
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DoubleVector upperb(nvars);
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DoubleVector init(nvars);
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//DoubleVector initialstep(nvars, rho);
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IntVector param(nvars, 0);
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IntVector lbound(nvars, 0);
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IntVector rbounds(nvars, 0);
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IntVector trapped(nvars, 0);
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agomez |
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ulcessvp |
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/**
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* Internal variables for PSO
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*/
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// printf("nvars:%d\n",nvars);
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if (size == 0) size = pso_calc_swarm_size(nvars);
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DoubleMatrix pos(size, nvars, 0.0); // position matrix
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DoubleMatrix vel(size, nvars, 0.0); // velocity matrix
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DoubleMatrix pos_b(size, nvars, 0.0); // best position matrix
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DoubleVector fit(size); // particle fitness vector
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DoubleVector fit_b(size); // best fitness vector
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DoubleMatrix pos_nb(size, nvars, 0.0); // what is the best informed position for each particle
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IntMatrix comm(size, size, 0); // communications:who informs who rows : those who inform cols : those who are informed
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int improved; // whether solution->error was improved during the last iteration
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int d, step;
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double a, b;
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double rho1, rho2; //random numbers (coefficients)
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double w; //current omega
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Inform_fun inform_fun;
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Calc_inertia_fun calc_inertia_fun; // inertia weight update function
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int psosteps = 0;
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agomez |
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ulcessvp |
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if (seed == 0) seed = 1234;
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agomez |
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ulcessvp |
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handle.logMessage(LOGINFO, "Starting PSO with particles ", size, "\n");
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agomez |
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ulcessvp |
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// SELECT APPROPRIATE NHOOD UPDATE FUNCTION
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switch (nhood_strategy) {
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case PSO_NHOOD_GLOBAL:
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// comm matrix not used
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handle.logMessage(LOGINFO, "PSO will use global communication\n");
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inform_fun = &OptInfoPso::inform_global;
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break;
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case PSO_NHOOD_RING:
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handle.logMessage(LOGINFO, "PSO will use ring communication\n");
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init_comm_ring(comm);
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inform_fun = &OptInfoPso::inform_ring;
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break;
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case PSO_NHOOD_RANDOM:
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handle.logMessage(LOGINFO, "PSO will use random communication\n");
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init_comm_random(comm);
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inform_fun = &OptInfoPso::inform_random;
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break;
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}
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// SELECT APPROPRIATE INERTIA WEIGHT UPDATE FUNCTION
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switch (w_strategy) {
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case PSO_W_CONST:
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handle.logMessage(LOGINFO, "PSO will use constant inertia\n");
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calc_inertia_fun = &OptInfoPso::calc_inertia_const;
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break;
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case PSO_W_LIN_DEC:
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handle.logMessage(LOGINFO, "PSO will use linear decreasing inertia\n");
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calc_inertia_fun = &OptInfoPso::calc_inertia_lin_dec;
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break;
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}
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//
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//initialize omega using standard value
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//
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w = PSO_INERTIA;
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handle.logMessage(LOGINFO, "PSO initial inertia ", w, "\n");
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handle.logMessage(LOGINFO, "PSO c1", c1, "\n");
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handle.logMessage(LOGINFO, "PSO c2", c2, "\n");
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handle.logMessage(LOGINFO, "PSO goal", goal, "\n");
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agomez |
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ulcessvp |
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// if (scale)
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EcoSystem->scaleVariables();
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//#ifdef _OPENMP
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// int numThr = omp_get_max_threads ( );
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//#pragma omp parallel for
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// for (i=0;i<numThr;i++)
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// EcoSystems[i]->scaleVariables();
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//#endif
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EcoSystem->getOptScaledValues(x);
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EcoSystem->getOptLowerBounds(lowerb);
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EcoSystem->getOptUpperBounds(upperb);
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EcoSystem->getOptInitialValues(init);
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for (i = 0; i < nvars; ++i) {
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bestx[i] = x[i];
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param[i] = i;
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agomez |
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}
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ulcessvp |
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//se escala?
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// if (scale)
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for (i = 0; i < nvars; i++) {
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// Scaling the bounds, because the parameters are scaled
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lowerb[i] = lowerb[i] / init[i];
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upperb[i] = upperb[i] / init[i];
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if (lowerb[i] > upperb[i]) {
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tmp = lowerb[i];
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lowerb[i] = upperb[i];
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upperb[i] = tmp;
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}
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}
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agomez |
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ulcessvp |
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bestf = EcoSystem->SimulateAndUpdate(x);
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if (bestf != bestf) { //check for NaN
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handle.logMessage(LOGINFO, "Error starting PSO optimisation with f(x) = infinity");
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converge = -1;
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iters = 1;
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return;
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}
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agomez |
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ulcessvp |
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offset = EcoSystem->getFuncEval(); //number of function evaluations done before loop
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// newf = bestf;
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// oldf = bestf;
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iters = 0;
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agomez |
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//
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ulcessvp |
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// Initialize the particles
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//
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i = 0;
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handle.logMessage(LOGINFO, "Initialising PSO ", size, "particles\n");
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// SWARM INITIALIZATION
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//
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// Initialize the fist particle with the best known position
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//
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for (d = 0; d < nvars; d++) {
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// generate two numbers within the specified range and around trialx
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a = x[d];
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// initialize position
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pos[i][d] = a;
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pos_b[i][d] = a;
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pos_nb[i][d] = a;
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// a = lowerb[d] + (upperb[d] - lowerb[d]) * (rand() * 1.0 / RAND_MAX);
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b = lowerb[d] + (upperb[d] - lowerb[d]) * (rand_r(&seed) * 1.0 / RAND_MAX);
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vel[i][d] = (a - b) / 2.;
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}
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fit[i] = EcoSystem->SimulateAndUpdate(pos[i]);
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fit_b[i] = fit[i]; // this is also the personal best
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if (fit[i] < bestf) {
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bestf = fit[i];
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bestx = pos[i];
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}
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agomez |
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// Now other particles with a random position in the solution space
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//
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ulcessvp |
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//#pragma omp parallel for default(shared) private(i,d,a,b)
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for (i = 1; i < size; i++) {
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ostringstream spos, svel;
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spos << "Initial position of particle " << i << " [";
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svel << "Initial velocity of particle " << i << " [";
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// for each dimension
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for (d = 0; d < nvars; d++) {
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// generate two numbers within the specified range and around trialx
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a = lowerb[d] + (upperb[d] - lowerb[d]) * (rand_r(&seed) * 1.0 / RAND_MAX);
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b = lowerb[d] + (upperb[d] - lowerb[d]) * (rand_r(&seed) * 1.0 / RAND_MAX);
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// initialize position
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pos[i][d] = a;
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// best position is trialx
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pos_b[i][d] = a;
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pos_nb[i][d] = a;
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// initialize velocity
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vel[i][d] = (a - b) / 2.;
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agomez |
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spos << pos[i][d] << " ";
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svel << vel[i][d] << " ";
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ulcessvp |
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}
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//#ifdef _OPENMP
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// fit[i] = EcoSystems[omp_get_thread_num ()]->SimulateAndUpdate(pos[i]);
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//#else
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fit[i] = EcoSystem->SimulateAndUpdate(pos[i]);
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//#endif
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fit_b[i] = fit[i]; // this is also the personal best
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// update gbest??
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agomez |
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//#pragma omp critical (best1)
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ulcessvp |
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if (fit[i] < bestf) {
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// update best fitness
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bestf = fit[i];
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// copy particle pos to gbest vector
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//memmove((void *)&bestx, (void *)&pos[i],
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// sizeof(double) * nvars);
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bestx = pos[i];
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}
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spos << " ]";
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svel << " ]";
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agomez |
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handle.logMessage(LOGDEBUG, spos.str().c_str());
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handle.logMessage(LOGDEBUG, svel.str().c_str());
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ulcessvp |
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}
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agomez |
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//
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ulcessvp |
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// RUN ALGORITHM
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agomez |
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//
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ulcessvp |
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psosteps = 0;
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while (1) {
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agomez |
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ulcessvp |
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handle.logMessage(LOGINFO, "PSO optimisation after", psosteps * size, "\n");
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if (isZero(bestf)) {
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iters = EcoSystem->getFuncEval() - offset;
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handle.logMessage(LOGINFO, "Error in PSO optimisation after", iters, "function evaluations, f(x) = 0");
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converge = -1;
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return;
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}
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/* if too many function evaluations occur, terminate the algorithm */
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agomez |
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ulcessvp |
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iters = EcoSystem->getFuncEval() - offset;
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if (psosteps > psoiter) {
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handle.logMessage(LOGINFO, "\nStopping PSO optimisation algorithm\n");
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handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
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handle.logMessage(LOGINFO, "The optimisation stopped because the maximum number of PSO steps was reached");
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handle.logMessage(LOGINFO, "was reached and NOT because an optimum was found for this run");
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agomez |
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ulcessvp |
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score = EcoSystem->SimulateAndUpdate(bestx);
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for (i = 0; i < nvars; i++)
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bestx[i] = bestx[i] * init[i];
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handle.logMessage(LOGINFO, "\nPSO finished with a likelihood score of", score);
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EcoSystem->storeVariables(score, bestx);
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return;
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}
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// update inertia weight
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if (w_strategy) w = (this->*calc_inertia_fun)(psosteps);
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// check optimization goal
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if (bestf <= goal) {
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handle.logMessage(LOGINFO, "\nStopping PSO optimisation algorithm\n");
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handle.logMessage(LOGINFO, "Goal achieved!!! @ step ", psosteps);
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score = EcoSystem->SimulateAndUpdate(bestx);
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handle.logMessage(LOGINFO, "\nPSO finished with a likelihood score of", score);
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break;
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}
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agomez |
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ulcessvp |
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// update pos_nb matrix (find best of neighborhood for all particles)
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(this->*inform_fun)(comm, pos_nb, pos_b, fit_b, bestx, improved);
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agomez |
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ulcessvp |
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// the value of improved was just used; reset it
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improved = 0;
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agomez |
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ulcessvp |
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// update all particles
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//#pragma omp parallel for private(d,rho1,rho2) default(shared)
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for (i = 0; i < size; i++) {
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// handle.logMessage(LOGDEBUG, "In parallel",omp_get_thread_num(),"\n");
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ostringstream spos, svel;
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spos << "New position of particle " << i << " [";
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svel << "New velocity of particle " << i << " [";
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// for each dimension
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for (d = 0; d < nvars; d++) {
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// calculate stochastic coefficients
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rho1 = c1 * (rand_r(&seed) * 1.0) / RAND_MAX;
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rho2 = c2 * (rand_r(&seed) * 1.0) / RAND_MAX;
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// update velocity
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vel[i][d] = w * vel[i][d] + rho1 * (pos_b[i][d] - pos[i][d]) + rho2 * (pos_nb[i][d] - pos[i][d]);
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// update position
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pos[i][d] += vel[i][d];
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spos << pos[i][d] << " ";
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svel << vel[i][d] << " ";
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291 : |
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// clamp position within bounds?
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if (clamp_pos) {
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if (pos[i][d] < lowerb[d]) {
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pos[i][d] = lowerb[d];
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vel[i][d] = 0;
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}
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297 : |
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else if (pos[i][d] > upperb[d]) {
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pos[i][d] = upperb[d];
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vel[i][d] = 0;
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}
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}
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else {
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// enforce periodic boundary conditions
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if (pos[i][d] < lowerb[d]) {
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pos[i][d] = upperb[d] - fmod(lowerb[d] - pos[i][d], upperb[d] - lowerb[d]);
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306 : |
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vel[i][d] = 0;
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307 : |
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}
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308 : |
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else if (pos[i][d] > upperb[d]) {
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309 : |
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pos[i][d] = lowerb[d] + fmod(pos[i][d] - upperb[d], upperb[d] - lowerb[d]);
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310 : |
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vel[i][d] = 0;
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311 : |
agomez |
22 |
|
312 : |
ulcessvp |
28 |
}
|
313 : |
|
|
}
|
314 : |
|
|
} //end for nvars
|
315 : |
agomez |
22 |
|
316 : |
ulcessvp |
28 |
spos << "]" << endl;
|
317 : |
|
|
svel << "]" << endl;
|
318 : |
|
|
handle.logMessage(LOGDEBUG, spos.str().c_str());
|
319 : |
|
|
handle.logMessage(LOGDEBUG, svel.str().c_str());
|
320 : |
agomez |
22 |
|
321 : |
ulcessvp |
28 |
// update particle fitness
|
322 : |
|
|
// fit[i] = EcoSystems[omp_get_thread_num()]->SimulateAndUpdate(pos[i]);
|
323 : |
|
|
fit[i] = EcoSystem->SimulateAndUpdate(pos[i]);
|
324 : |
|
|
// update personal best position?
|
325 : |
|
|
if (fit[i] < fit_b[i]) {
|
326 : |
|
|
fit_b[i] = fit[i];
|
327 : |
|
|
pos_b[i] = pos[i];
|
328 : |
|
|
}
|
329 : |
|
|
// update gbest??
|
330 : |
|
|
handle.logMessage(LOGDEBUG, "Particle ", i);
|
331 : |
|
|
handle.logMessage(LOGDEBUG, "The likelihood score is", bestf, "at the point");
|
332 : |
|
|
handle.logMessage(LOGDEBUG, "Its likelihood score is", fit[i], "at the point");
|
333 : |
|
|
//#pragma omp critical (best2)
|
334 : |
|
|
if (fit[i] < bestf) {
|
335 : |
|
|
improved = 1;
|
336 : |
|
|
// update best fitness
|
337 : |
|
|
bestf = fit[i];
|
338 : |
|
|
for (d = 0; d < nvars; d++)
|
339 : |
|
|
bestx[d] = pos[i][d] * init[d];
|
340 : |
|
|
iters = EcoSystem->getFuncEval() - offset;
|
341 : |
|
|
handle.logMessage(LOGINFO, "\nNew optimum found after", iters, "function evaluations");
|
342 : |
|
|
handle.logMessage(LOGINFO, "The likelihood score is", bestf, "at the point");
|
343 : |
|
|
EcoSystem->storeVariables(bestf, bestx);
|
344 : |
|
|
EcoSystem->writeBestValues();
|
345 : |
|
|
bestx = pos[i];
|
346 : |
agomez |
22 |
|
347 : |
ulcessvp |
28 |
}
|
348 : |
|
|
} // end i
|
349 : |
agomez |
22 |
|
350 : |
ulcessvp |
28 |
psosteps++;
|
351 : |
agomez |
22 |
|
352 : |
|
|
} // while (newf < bestf)
|
353 : |
|
|
|
354 : |
|
|
iters = EcoSystem->getFuncEval() - offset;
|
355 : |
|
|
handle.logMessage(LOGINFO, "Existing PSO after ", iters, "function evaluations ...");
|
356 : |
ulcessvp |
28 |
for (d = 0; d < nvars; d++)
|
357 : |
|
|
bestx[d] = bestx[d] * init[d];
|
358 : |
agomez |
22 |
EcoSystem->storeVariables(bestf, bestx);
|
359 : |
|
|
EcoSystem->writeBestValues();
|
360 : |
|
|
|
361 : |
|
|
}
|
362 : |
|
|
|
363 : |
ulcessvp |
28 |
#ifdef _OPENMP
|
364 : |
agomez |
22 |
void OptInfoPso::OptimiseLikelihoodREP() {
|
365 : |
ulcessvp |
28 |
OptimiseLikelihood();
|
366 : |
agomez |
22 |
|
367 : |
|
|
}
|
368 : |
|
|
|
369 : |
|
|
void OptInfoPso::OptimiseLikelihoodOMP() {
|
370 : |
|
|
|
371 : |
ulcessvp |
28 |
handle.logMessage(LOGINFO, "\nStarting PSO optimisation algorithm\n");
|
372 : |
|
|
double tmp;
|
373 : |
|
|
int i, offset, rchange, rcheck, rnumber;
|
374 : |
|
|
int nvars = EcoSystem->numOptVariables();
|
375 : |
|
|
DoubleVector x(nvars);
|
376 : |
|
|
// DoubleVector trialx(nvars);
|
377 : |
|
|
DoubleVector bestx(nvars);
|
378 : |
|
|
DoubleVector scalex(nvars);
|
379 : |
|
|
DoubleVector lowerb(nvars);
|
380 : |
|
|
DoubleVector upperb(nvars);
|
381 : |
|
|
DoubleVector init(nvars);
|
382 : |
|
|
//DoubleVector initialstep(nvars, rho);
|
383 : |
|
|
IntVector param(nvars, 0);
|
384 : |
|
|
IntVector lbound(nvars, 0);
|
385 : |
|
|
IntVector rbounds(nvars, 0);
|
386 : |
|
|
IntVector trapped(nvars, 0);
|
387 : |
|
|
struct Best { double bestf; int index;};
|
388 : |
|
|
struct Best best;
|
389 : |
|
|
|
390 : |
|
|
/**
|
391 : |
|
|
* Internal variables for PSO
|
392 : |
|
|
*/
|
393 : |
|
|
// printf("nvars:%d\n",nvars);
|
394 : |
|
|
if (size == 0) size = pso_calc_swarm_size(nvars);
|
395 : |
|
|
DoubleMatrix pos(size, nvars, 0.0); // position matrix
|
396 : |
|
|
DoubleMatrix vel(size, nvars, 0.0); // velocity matrix
|
397 : |
|
|
DoubleMatrix pos_b(size, nvars, 0.0); // best position matrix
|
398 : |
|
|
DoubleVector fit(size); // particle fitness vector
|
399 : |
|
|
DoubleVector fit_b(size); // best fitness vector
|
400 : |
|
|
DoubleMatrix pos_nb(size, nvars, 0.0); // what is the best informed position for each particle
|
401 : |
|
|
IntMatrix comm(size, size, 0); // communications:who informs who rows : those who inform cols : those who are informed
|
402 : |
|
|
int improved; // whether solution->error was improved during the last iteration
|
403 : |
|
|
int d, step;
|
404 : |
|
|
double a, b;
|
405 : |
|
|
double rho1, rho2; //random numbers (coefficients)
|
406 : |
|
|
double w; //current omega
|
407 : |
|
|
Inform_fun inform_fun;
|
408 : |
|
|
Calc_inertia_fun calc_inertia_fun; // inertia weight update function
|
409 : |
|
|
int psosteps = 0; //!= iters?
|
410 : |
|
|
|
411 : |
|
|
// if (seed == 0) seed = 1234;
|
412 : |
|
|
|
413 : |
|
|
handle.logMessage(LOGINFO, "Starting PSO with particles ", size, "\n");
|
414 : |
|
|
|
415 : |
|
|
// SELECT APPROPRIATE NHOOD UPDATE FUNCTION
|
416 : |
|
|
switch (nhood_strategy) {
|
417 : |
|
|
case PSO_NHOOD_GLOBAL:
|
418 : |
|
|
// comm matrix not used
|
419 : |
|
|
handle.logMessage(LOGINFO, "PSO will use global communication\n");
|
420 : |
|
|
inform_fun = &OptInfoPso::inform_global;
|
421 : |
|
|
break;
|
422 : |
|
|
case PSO_NHOOD_RING:
|
423 : |
|
|
handle.logMessage(LOGINFO, "PSO will use ring communication\n");
|
424 : |
|
|
init_comm_ring(comm);
|
425 : |
|
|
inform_fun = &OptInfoPso::inform_ring;
|
426 : |
|
|
break;
|
427 : |
|
|
case PSO_NHOOD_RANDOM:
|
428 : |
|
|
handle.logMessage(LOGINFO, "PSO will use random communication\n");
|
429 : |
|
|
init_comm_random(comm);
|
430 : |
|
|
inform_fun = &OptInfoPso::inform_random;
|
431 : |
|
|
break;
|
432 : |
|
|
}
|
433 : |
|
|
// SELECT APPROPRIATE INERTIA WEIGHT UPDATE FUNCTION
|
434 : |
|
|
switch (w_strategy) {
|
435 : |
|
|
case PSO_W_CONST:
|
436 : |
|
|
handle.logMessage(LOGINFO, "PSO will use constant inertia\n");
|
437 : |
|
|
calc_inertia_fun = &OptInfoPso::calc_inertia_const;
|
438 : |
|
|
break;
|
439 : |
|
|
case PSO_W_LIN_DEC:
|
440 : |
|
|
handle.logMessage(LOGINFO, "PSO will use linear decreasing inertia\n");
|
441 : |
|
|
calc_inertia_fun = &OptInfoPso::calc_inertia_lin_dec;
|
442 : |
|
|
break;
|
443 : |
|
|
}
|
444 : |
|
|
//
|
445 : |
|
|
//initialize omega using standard value
|
446 : |
|
|
//
|
447 : |
|
|
w = PSO_INERTIA;
|
448 : |
|
|
handle.logMessage(LOGINFO, "PSO initial inertia ", w, "\n");
|
449 : |
|
|
handle.logMessage(LOGINFO, "PSO c1", c1, "\n");
|
450 : |
|
|
handle.logMessage(LOGINFO, "PSO c2", c2, "\n");
|
451 : |
|
|
handle.logMessage(LOGINFO, "PSO goal", goal, "\n");
|
452 : |
|
|
|
453 : |
|
|
EcoSystem->scaleVariables();
|
454 : |
|
|
int numThr = omp_get_max_threads ( );
|
455 : |
|
|
// if (scale) {
|
456 : |
|
|
#pragma omp parallel for
|
457 : |
|
|
for (i=0;i<numThr;i++)
|
458 : |
|
|
EcoSystems[i]->scaleVariables();
|
459 : |
|
|
// }
|
460 : |
|
|
//#endif
|
461 : |
|
|
EcoSystem->getOptScaledValues(x);
|
462 : |
|
|
EcoSystem->getOptLowerBounds(lowerb);
|
463 : |
|
|
EcoSystem->getOptUpperBounds(upperb);
|
464 : |
|
|
EcoSystem->getOptInitialValues(init);
|
465 : |
|
|
|
466 : |
|
|
|
467 : |
|
|
for (i = 0; i < nvars; ++i) {
|
468 : |
|
|
bestx[i] = x[i];
|
469 : |
|
|
param[i] = i;
|
470 : |
|
|
}
|
471 : |
|
|
//se escala?
|
472 : |
|
|
// if (scale)
|
473 : |
|
|
for (i = 0; i < nvars; i++) {
|
474 : |
|
|
scalex[i] = x[i];
|
475 : |
|
|
// Scaling the bounds, because the parameters are scaled
|
476 : |
|
|
lowerb[i] = lowerb[i] / init[i];
|
477 : |
|
|
upperb[i] = upperb[i] / init[i];
|
478 : |
|
|
if (lowerb[i] > upperb[i]) {
|
479 : |
|
|
tmp = lowerb[i];
|
480 : |
|
|
lowerb[i] = upperb[i];
|
481 : |
|
|
upperb[i] = tmp;
|
482 : |
|
|
}
|
483 : |
|
|
}
|
484 : |
|
|
|
485 : |
|
|
best.bestf = EcoSystem->SimulateAndUpdate(x);
|
486 : |
|
|
best.index=0;
|
487 : |
|
|
if (best.bestf != best.bestf) { //check for NaN
|
488 : |
|
|
handle.logMessage(LOGINFO, "Error starting PSO optimisation with f(x) = infinity");
|
489 : |
|
|
converge = -1;
|
490 : |
|
|
iters = 1;
|
491 : |
|
|
return;
|
492 : |
|
|
}
|
493 : |
|
|
|
494 : |
|
|
offset = EcoSystem->getFuncEval(); //number of function evaluations done before loop
|
495 : |
|
|
// newf = bestf;
|
496 : |
|
|
// oldf = bestf;
|
497 : |
|
|
|
498 : |
|
|
iters = 0;
|
499 : |
|
|
|
500 : |
|
|
//
|
501 : |
|
|
// Initialize the particles
|
502 : |
|
|
//
|
503 : |
|
|
i = 0;
|
504 : |
|
|
handle.logMessage(LOGINFO, "Initialising PSO ", size, "particles\n");
|
505 : |
|
|
|
506 : |
|
|
// SWARM INITIALIZATION
|
507 : |
|
|
//
|
508 : |
|
|
// Initialize the fist particle with the best known position
|
509 : |
|
|
//
|
510 : |
|
|
// best.bestf=bestf;
|
511 : |
|
|
|
512 : |
|
|
for (d = 0; d < nvars; d++) {
|
513 : |
|
|
// generate two numbers within the specified range and around trialx
|
514 : |
|
|
a = x[d];
|
515 : |
|
|
// initialize position
|
516 : |
|
|
pos[i][d] = a;
|
517 : |
|
|
pos_b[i][d] = a;
|
518 : |
|
|
pos_nb[i][d] = a;
|
519 : |
|
|
// a = lowerb[d] + (upperb[d] - lowerb[d]) * (rand() * 1.0 / RAND_MAX);
|
520 : |
|
|
b = lowerb[d] + (upperb[d] - lowerb[d]) * (rand_r(&seed) * 1.0 / RAND_MAX);
|
521 : |
|
|
vel[i][d] = (a - b) / 2.;
|
522 : |
|
|
}
|
523 : |
|
|
fit[i] = EcoSystem->SimulateAndUpdate(pos[i]);
|
524 : |
|
|
fit_b[i] = fit[i]; // this is also the personal best
|
525 : |
|
|
if (fit[i] < best.bestf) {
|
526 : |
|
|
best.bestf = fit[i];
|
527 : |
|
|
// best.bestf=bestf;
|
528 : |
|
|
bestx = pos[i];
|
529 : |
|
|
best.index=i;
|
530 : |
|
|
}
|
531 : |
|
|
|
532 : |
|
|
// Now other particles with a random position in the solution space
|
533 : |
|
|
//
|
534 : |
|
|
// handle.logMessage(LOGINFO, "ini 1 best.bestf=",best.bestf, "\n");
|
535 : |
|
|
|
536 : |
|
|
#pragma omp declare reduction(bestomp: Best : omp_out = omp_in.bestf < omp_out.bestf ? omp_in : omp_out) initializer(omp_priv=omp_orig)
|
537 : |
|
|
#pragma omp parallel for default(shared) private(i,d,a,b) reduction(bestomp:best) //firstprivate(bestf)
|
538 : |
|
|
for (i = 1; i < size; i++) {
|
539 : |
|
|
ostringstream spos, svel;
|
540 : |
|
|
spos << "Initial position of particle " << i << " [";
|
541 : |
|
|
svel << "Initial velocity of particle " << i << " [";
|
542 : |
|
|
// for each dimension
|
543 : |
|
|
for (d = 0; d < nvars; d++) {
|
544 : |
|
|
// generate two numbers within the specified range and around trialx
|
545 : |
|
|
a = lowerb[d] + (upperb[d] - lowerb[d]) * (rand_r(&seed) * 1.0 / RAND_MAX);
|
546 : |
|
|
b = lowerb[d] + (upperb[d] - lowerb[d]) * (rand_r(&seed) * 1.0 / RAND_MAX);
|
547 : |
|
|
// initialize position
|
548 : |
|
|
pos[i][d] = a;
|
549 : |
|
|
// best position is trialx
|
550 : |
|
|
pos_b[i][d] = a;
|
551 : |
|
|
pos_nb[i][d] = a;
|
552 : |
|
|
// initialize velocity
|
553 : |
|
|
vel[i][d] = (a - b) / 2.;
|
554 : |
|
|
spos << pos[i][d] << " ";
|
555 : |
|
|
svel << vel[i][d] << " ";
|
556 : |
|
|
}
|
557 : |
|
|
//#ifdef _OPENMP
|
558 : |
|
|
fit[i] = EcoSystems[omp_get_thread_num ()]->SimulateAndUpdate(pos[i]);
|
559 : |
|
|
// best.bestf = fit[i];
|
560 : |
|
|
//#else
|
561 : |
|
|
// fit[i] = EcoSystem->SimulateAndUpdate(pos[i]);
|
562 : |
|
|
//#endif
|
563 : |
|
|
fit_b[i] = fit[i]; // this is also the personal best
|
564 : |
|
|
// update gbest??
|
565 : |
|
|
//#pragma omp critical (best1)
|
566 : |
|
|
if (fit[i] < best.bestf) {
|
567 : |
|
|
// update best fitness
|
568 : |
|
|
// bestf = fit[i];
|
569 : |
|
|
// copy particle pos to gbest vector
|
570 : |
|
|
//memmove((void *)&bestx, (void *)&pos[i],
|
571 : |
|
|
// sizeof(double) * nvars);
|
572 : |
|
|
// bestx = pos[i];
|
573 : |
|
|
best.index=i;
|
574 : |
|
|
// bestf=best.bestf;
|
575 : |
|
|
best.bestf = fit[i];
|
576 : |
|
|
}
|
577 : |
|
|
spos << " ]";
|
578 : |
|
|
svel << " ]";
|
579 : |
|
|
handle.logMessage(LOGDEBUG, spos.str().c_str());
|
580 : |
|
|
handle.logMessage(LOGDEBUG, svel.str().c_str());
|
581 : |
|
|
|
582 : |
|
|
}
|
583 : |
|
|
|
584 : |
|
|
//
|
585 : |
|
|
// Compare the reduction with the best value
|
586 : |
|
|
//
|
587 : |
|
|
|
588 : |
|
|
// if (best.bestf < bestf){
|
589 : |
|
|
// bestx=pos[best.index];
|
590 : |
|
|
// bestf=best.bestf;
|
591 : |
|
|
// }
|
592 : |
|
|
|
593 : |
|
|
//
|
594 : |
|
|
// RUN ALGORITHM
|
595 : |
|
|
//
|
596 : |
|
|
// handle.logMessage(LOGINFO, "ini 2 best.bestf=",best.bestf, "\n");
|
597 : |
|
|
psosteps = 0;
|
598 : |
|
|
while (1) {
|
599 : |
|
|
|
600 : |
|
|
handle.logMessage(LOGINFO, "PSO optimisation after", psosteps * size, "\n");
|
601 : |
|
|
if (isZero(best.bestf)) {
|
602 : |
|
|
iters = (EcoSystems[0]->getFuncEval() * numThr) + EcoSystem->getFuncEval() - offset;
|
603 : |
|
|
handle.logMessage(LOGINFO, "Error in PSO optimisation after", iters, "function evaluations, f(x) = 0");
|
604 : |
|
|
converge = -1;
|
605 : |
|
|
return;
|
606 : |
|
|
}
|
607 : |
|
|
/* if too many function evaluations occur, terminate the algorithm */
|
608 : |
|
|
|
609 : |
|
|
iters = (EcoSystems[0]->getFuncEval() * numThr) + EcoSystem->getFuncEval() - offset;
|
610 : |
|
|
if (psosteps > psoiter) {
|
611 : |
|
|
handle.logMessage(LOGINFO, "\nStopping PSO optimisation algorithm\n");
|
612 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
|
613 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped because the maximum number of PSO steps was reached");
|
614 : |
|
|
handle.logMessage(LOGINFO, "was reached and NOT because an optimum was found for this run");
|
615 : |
|
|
|
616 : |
|
|
score = EcoSystem->SimulateAndUpdate(bestx);
|
617 : |
|
|
for (i = 0; i < nvars; i++)
|
618 : |
|
|
bestx[i] = bestx[i] * init[i];
|
619 : |
|
|
handle.logMessage(LOGINFO, "\nPSO finished with a likelihood score of", score);
|
620 : |
|
|
EcoSystem->storeVariables(score, bestx);
|
621 : |
|
|
return;
|
622 : |
|
|
}
|
623 : |
|
|
// update inertia weight
|
624 : |
|
|
if (w_strategy) w = (this->*calc_inertia_fun)(psosteps);
|
625 : |
|
|
// check optimization goal
|
626 : |
|
|
if (best.bestf <= goal) {
|
627 : |
|
|
handle.logMessage(LOGINFO, "\nStopping PSO optimisation algorithm\n");
|
628 : |
|
|
handle.logMessage(LOGINFO, "Goal achieved!!! @ step ", psosteps);
|
629 : |
|
|
score = EcoSystem->SimulateAndUpdate(bestx);
|
630 : |
|
|
handle.logMessage(LOGINFO, "\nPSO finished with a likelihood score of", score);
|
631 : |
|
|
break;
|
632 : |
|
|
}
|
633 : |
|
|
|
634 : |
|
|
// update pos_nb matrix (find best of neighborhood for all particles)
|
635 : |
|
|
(this->*inform_fun)(comm, pos_nb, pos_b, fit_b, bestx, improved);
|
636 : |
|
|
|
637 : |
|
|
// the value of improved was just used; reset it
|
638 : |
|
|
improved = 0;
|
639 : |
|
|
|
640 : |
|
|
// update all particles
|
641 : |
|
|
// best.bestf=bestf;
|
642 : |
|
|
//#pragma omp declare reduction(bestomp2: Best : omp_out = omp_in.bestf < omp_out.bestf ? omp_in : omp_out) initializer(omp_priv=omp_orig)
|
643 : |
|
|
#pragma omp parallel for default(shared) private(i,d,rho1,rho2) reduction(bestomp:best) //firstprivate(best)
|
644 : |
|
|
for (i = 0; i < size; i++) {
|
645 : |
|
|
// handle.logMessage(LOGDEBUG, "In parallel",omp_get_thread_num(),"\n");
|
646 : |
|
|
ostringstream spos, svel;
|
647 : |
|
|
spos << "New position of particle " << i << " [";
|
648 : |
|
|
svel << "New velocity of particle " << i << " [";
|
649 : |
|
|
// for each dimension
|
650 : |
|
|
for (d = 0; d < nvars; d++) {
|
651 : |
|
|
// calculate stochastic coefficients
|
652 : |
|
|
rho1 = c1 * (rand_r(&seed) * 1.0) / RAND_MAX;
|
653 : |
|
|
rho2 = c2 * (rand_r(&seed) * 1.0) / RAND_MAX;
|
654 : |
|
|
// update velocity
|
655 : |
|
|
vel[i][d] = w * vel[i][d] + rho1 * (pos_b[i][d] - pos[i][d]) + rho2 * (pos_nb[i][d] - pos[i][d]);
|
656 : |
|
|
// update position
|
657 : |
|
|
pos[i][d] += vel[i][d];
|
658 : |
|
|
spos << pos[i][d] << " ";
|
659 : |
|
|
svel << vel[i][d] << " ";
|
660 : |
|
|
// clamp position within bounds?
|
661 : |
|
|
if (clamp_pos) {
|
662 : |
|
|
if (pos[i][d] < lowerb[d]) {
|
663 : |
|
|
pos[i][d] = lowerb[d];
|
664 : |
|
|
vel[i][d] = 0;
|
665 : |
|
|
}
|
666 : |
|
|
else if (pos[i][d] > upperb[d]) {
|
667 : |
|
|
pos[i][d] = upperb[d];
|
668 : |
|
|
vel[i][d] = 0;
|
669 : |
|
|
}
|
670 : |
|
|
}
|
671 : |
|
|
else {
|
672 : |
|
|
// enforce periodic boundary conditions
|
673 : |
|
|
if (pos[i][d] < lowerb[d]) {
|
674 : |
|
|
pos[i][d] = upperb[d] - fmod(lowerb[d] - pos[i][d], upperb[d] - lowerb[d]);
|
675 : |
|
|
vel[i][d] = 0;
|
676 : |
|
|
}
|
677 : |
|
|
else if (pos[i][d] > upperb[d]) {
|
678 : |
|
|
pos[i][d] = lowerb[d] + fmod(pos[i][d] - upperb[d], upperb[d] - lowerb[d]);
|
679 : |
|
|
vel[i][d] = 0;
|
680 : |
|
|
|
681 : |
|
|
}
|
682 : |
|
|
}
|
683 : |
|
|
} //end for nvars
|
684 : |
|
|
|
685 : |
|
|
spos << "]" << endl;
|
686 : |
|
|
svel << "]" << endl;
|
687 : |
|
|
handle.logMessage(LOGDEBUG, spos.str().c_str());
|
688 : |
|
|
handle.logMessage(LOGDEBUG, svel.str().c_str());
|
689 : |
|
|
|
690 : |
|
|
// update particle fitness
|
691 : |
|
|
fit[i] = EcoSystems[omp_get_thread_num()]->SimulateAndUpdate(pos[i]);
|
692 : |
|
|
// fit[i] = EcoSystem->SimulateAndUpdate(pos[i]);
|
693 : |
|
|
// update personal best position?
|
694 : |
|
|
// best.bestf = fit[i];
|
695 : |
|
|
if (fit[i] < fit_b[i]) {
|
696 : |
|
|
fit_b[i] = fit[i];
|
697 : |
|
|
pos_b[i] = pos[i];
|
698 : |
|
|
}
|
699 : |
|
|
// update gbest??
|
700 : |
|
|
handle.logMessage(LOGDEBUG, "Particle ", i);
|
701 : |
|
|
handle.logMessage(LOGDEBUG, "The likelihood score is", best.bestf, "at the point");
|
702 : |
|
|
handle.logMessage(LOGDEBUG, "Its likelihood score is", fit[i], "at the point");
|
703 : |
|
|
//#pragma omp critical (best2)
|
704 : |
|
|
if (fit[i] < best.bestf) {
|
705 : |
|
|
improved = 1;
|
706 : |
|
|
// update best fitness
|
707 : |
|
|
best.index=i;
|
708 : |
|
|
best.bestf = fit[i];
|
709 : |
|
|
for (d = 0; d < nvars; d++)
|
710 : |
|
|
bestx[d] = pos[i][d] * init[d];
|
711 : |
|
|
iters = (EcoSystems[0]->getFuncEval() * numThr) + EcoSystem->getFuncEval() - offset;
|
712 : |
|
|
handle.logMessage(LOGINFO, "\nNew optimum found after", iters, "function evaluations");
|
713 : |
|
|
handle.logMessage(LOGINFO, "The likelihood score is", best.bestf, "at the point");
|
714 : |
|
|
EcoSystem->storeVariables(best.bestf, bestx);
|
715 : |
|
|
EcoSystem->writeBestValues();
|
716 : |
|
|
bestx = pos[i];
|
717 : |
|
|
|
718 : |
|
|
}
|
719 : |
|
|
} // end i
|
720 : |
|
|
// if (best.bestf < bestf){
|
721 : |
|
|
// bestf=best.bestf;
|
722 : |
|
|
// bestx=pos[best.index];
|
723 : |
|
|
// for (d = 0; d < nvars; d++)
|
724 : |
|
|
// bestx[d] = bestx[d] * init[d];
|
725 : |
|
|
// iters = EcoSystem->getFuncEval() - offset; //To change. It is incorrect when is parallel.
|
726 : |
|
|
// handle.logMessage(LOGINFO, "\nNew optimum found after", iters, "function evaluations");
|
727 : |
|
|
// handle.logMessage(LOGINFO, "The likelihood score is", bestf, "at the point");
|
728 : |
|
|
// EcoSystem->storeVariables(bestf, bestx);
|
729 : |
|
|
// EcoSystem->writeBestValues();
|
730 : |
|
|
// bestx=pos[best.index];
|
731 : |
|
|
// }
|
732 : |
|
|
// handle.logMessage(LOGINFO, "best.bestf=",best.bestf, "\n");
|
733 : |
|
|
psosteps += numThr;
|
734 : |
|
|
|
735 : |
|
|
} // while (newf < bestf)
|
736 : |
|
|
|
737 : |
|
|
iters = (EcoSystems[0]->getFuncEval() * numThr) + EcoSystem->getFuncEval() - offset;
|
738 : |
|
|
handle.logMessage(LOGINFO, "Existing PSO after ", iters, "function evaluations ...");
|
739 : |
|
|
for (d = 0; d < nvars; d++)
|
740 : |
|
|
bestx[d] = bestx[d] * init[d];
|
741 : |
|
|
EcoSystem->storeVariables(best.bestf, bestx);
|
742 : |
|
|
EcoSystem->writeBestValues();
|
743 : |
|
|
|
744 : |
|
|
}
|
745 : |
|
|
#endif
|
746 : |
|
|
|
747 : |
agomez |
22 |
/**
|
748 : |
|
|
* \brief calculate constant inertia weight equal to w_max
|
749 : |
|
|
*/
|
750 : |
|
|
double OptInfoPso::calc_inertia_const(int step) {
|
751 : |
ulcessvp |
28 |
return w_max;
|
752 : |
agomez |
22 |
}
|
753 : |
|
|
/**
|
754 : |
|
|
* \brief calculate linearly decreasing inertia weight
|
755 : |
|
|
*/
|
756 : |
|
|
double OptInfoPso::calc_inertia_lin_dec(int step) {
|
757 : |
|
|
|
758 : |
|
|
int dec_stage = 3 * psoiter / 4;
|
759 : |
|
|
if (step <= dec_stage)
|
760 : |
ulcessvp |
28 |
return w_min + (w_max - w_min) * (dec_stage - step) / dec_stage;
|
761 : |
agomez |
22 |
else
|
762 : |
ulcessvp |
28 |
return w_min;
|
763 : |
agomez |
22 |
}
|
764 : |
|
|
|
765 : |
|
|
/**
|
766 : |
|
|
* \brief NEIGHBORHOOD (COMM) MATRIX STRATEGIES. global neighborhood
|
767 : |
|
|
*/
|
768 : |
ulcessvp |
28 |
void OptInfoPso::inform_global(IntMatrix& comm, DoubleMatrix& pos_nb, DoubleMatrix& pos_b, DoubleVector& fit_b, DoubleVector& gbest, int improved) {
|
769 : |
agomez |
22 |
|
770 : |
|
|
int i;
|
771 : |
ulcessvp |
28 |
// all particles have the same attractor (gbest)
|
772 : |
|
|
// copy the contents of gbest to pos_nb
|
773 : |
|
|
for (i = 0; i < size; i++)
|
774 : |
|
|
//memmove((void *)&pos_nb[i][0], &gbest[0],
|
775 : |
agomez |
22 |
// sizeof(double) * gbest.Size());
|
776 : |
ulcessvp |
28 |
pos_nb[i] = gbest;
|
777 : |
agomez |
22 |
}
|
778 : |
|
|
|
779 : |
|
|
/**
|
780 : |
|
|
* \brief general inform function :: according to the connectivity matrix COMM, it copies the best position (from pos_b) of the informers of each particle to the pos_nb matrix
|
781 : |
|
|
*/
|
782 : |
ulcessvp |
28 |
void OptInfoPso::inform(IntMatrix& comm, DoubleMatrix& pos_nb, DoubleMatrix& pos_b, DoubleVector& fit_b, int improved) {
|
783 : |
agomez |
22 |
int i, j;
|
784 : |
|
|
int b_n; // best neighbor in terms of fitness
|
785 : |
|
|
|
786 : |
ulcessvp |
28 |
// for each particle
|
787 : |
|
|
for (j = 0; j < size; j++) {
|
788 : |
|
|
b_n = j; // self is best
|
789 : |
|
|
// who is the best informer??
|
790 : |
|
|
for (i = 0; i < size; i++)
|
791 : |
|
|
// the i^th particle informs the j^th particle
|
792 : |
|
|
if (comm[i][j] && fit_b[i] < fit_b[b_n])
|
793 : |
|
|
// found a better informer for j^th particle
|
794 : |
|
|
b_n = i;
|
795 : |
|
|
// copy pos_b of b_n^th particle to pos_nb[j]
|
796 : |
|
|
//memmove((void *)&pos_nb[j][0],
|
797 : |
|
|
// (void *)&pos_b[b_n][0],
|
798 : |
|
|
// sizeof(double) * pos_nb.Ncol(j));
|
799 : |
|
|
pos_nb[j] = pos_b[b_n];
|
800 : |
|
|
}
|
801 : |
agomez |
22 |
}
|
802 : |
|
|
|
803 : |
|
|
/**
|
804 : |
|
|
* \brief topology initialization :: this is a static (i.e. fixed) topology
|
805 : |
|
|
*/
|
806 : |
|
|
void OptInfoPso::init_comm_ring(IntMatrix& comm) {
|
807 : |
|
|
int i;
|
808 : |
ulcessvp |
28 |
// reset array
|
809 : |
|
|
comm.setToZero();
|
810 : |
agomez |
22 |
|
811 : |
ulcessvp |
28 |
// choose informers
|
812 : |
|
|
for (i = 0; i < size; i++) {
|
813 : |
|
|
// set diagonal to 1
|
814 : |
|
|
comm[i][i] = 1;
|
815 : |
|
|
if (i == 0) {
|
816 : |
|
|
// look right
|
817 : |
|
|
comm[i][1] = 1;
|
818 : |
|
|
// look left
|
819 : |
|
|
comm[i][size - 1] = 1;
|
820 : |
|
|
}
|
821 : |
|
|
else if (i == size - 1) {
|
822 : |
|
|
// look right
|
823 : |
|
|
comm[i][0] = 1;
|
824 : |
|
|
// look left
|
825 : |
|
|
comm[i][i - 1] = 1;
|
826 : |
|
|
}
|
827 : |
|
|
else {
|
828 : |
|
|
// look right
|
829 : |
|
|
comm[i][i + 1] = 1;
|
830 : |
|
|
// look left
|
831 : |
|
|
comm[i][i - 1] = 1;
|
832 : |
|
|
}
|
833 : |
agomez |
22 |
|
834 : |
|
|
}
|
835 : |
ulcessvp |
28 |
// Print Matrix
|
836 : |
agomez |
22 |
handle.logMessage(LOGDEBUG, "\nPSO ring communication\n");
|
837 : |
ulcessvp |
28 |
for (i = 0; i < size; i++) {
|
838 : |
|
|
string message = "[";
|
839 : |
|
|
for (int j = 0; j < size; j++) {
|
840 : |
|
|
std::ostringstream ss;
|
841 : |
|
|
ss << comm[i][j];
|
842 : |
|
|
message = message + " " + ss.str();
|
843 : |
|
|
}
|
844 : |
|
|
message = message + "]\n";
|
845 : |
|
|
handle.logMessage(LOGDEBUG, message.c_str());
|
846 : |
agomez |
22 |
}
|
847 : |
|
|
|
848 : |
|
|
}
|
849 : |
|
|
|
850 : |
ulcessvp |
28 |
void OptInfoPso::inform_ring(IntMatrix& comm, DoubleMatrix& pos_nb, DoubleMatrix& pos_b, DoubleVector& fit_b, DoubleVector& gbest, int improved) {
|
851 : |
agomez |
22 |
|
852 : |
ulcessvp |
28 |
// update pos_nb matrix
|
853 : |
agomez |
22 |
inform(comm, pos_nb, pos_b, fit_b, improved);
|
854 : |
|
|
|
855 : |
|
|
}
|
856 : |
|
|
|
857 : |
|
|
/**
|
858 : |
|
|
* \brief random neighborhood topology
|
859 : |
|
|
*/
|
860 : |
|
|
void OptInfoPso::init_comm_random(IntMatrix& comm) {
|
861 : |
|
|
|
862 : |
|
|
int i, j, k;
|
863 : |
ulcessvp |
28 |
// reset array
|
864 : |
agomez |
22 |
comm.setToZero();
|
865 : |
|
|
|
866 : |
ulcessvp |
28 |
// choose informers
|
867 : |
|
|
for (i = 0; i < size; i++) {
|
868 : |
|
|
// each particle informs itself
|
869 : |
|
|
comm[i][i] = 1;
|
870 : |
|
|
// choose kappa (on average) informers for each particle
|
871 : |
|
|
for (k = 0; k < nhood_size; k++) {
|
872 : |
|
|
// generate a random index
|
873 : |
|
|
//j = gsl_rng_uniform_int(settings->rng, settings->size);
|
874 : |
|
|
j = rand_r(&this->seed) * size / RAND_MAX;
|
875 : |
|
|
// particle i informs particle j
|
876 : |
|
|
comm[i][j] = 1;
|
877 : |
|
|
}
|
878 : |
agomez |
22 |
}
|
879 : |
|
|
}
|
880 : |
|
|
|
881 : |
ulcessvp |
28 |
void OptInfoPso::inform_random(IntMatrix& comm, DoubleMatrix& pos_nb, DoubleMatrix& pos_b, DoubleVector& fit_b, DoubleVector& gbest, int improved) {
|
882 : |
agomez |
22 |
|
883 : |
ulcessvp |
28 |
// regenerate connectivity??
|
884 : |
|
|
if (!improved) init_comm_random(comm);
|
885 : |
agomez |
22 |
inform(comm, pos_nb, pos_b, fit_b, improved);
|
886 : |
|
|
|
887 : |
|
|
}
|
888 : |
ulcessvp |
28 |
|