模拟退火算法解决路径优化的源代码

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?òóD simulated annealing with metropolies(Monte Carlo)×?μ?ò???????μ?′ú??￡???òa?′?′?′￡? void anneal(int nparam, int nstep, int nstep_per_block, double t0, const double * param_in,

double cost_in, double * params_out, double * cost_out) { int nblock; int step; int block; int nactive; int rank;

int n_accepted = 0; int i, j, n;

double cost_current, cost_trial; int * param_index; double * param_current; double * param_trial; double * Q; double * S; double * u; double * dp; double * A;

FILE * fp_log_file;

char fname[FILENAME_MAX]; double temp = t0;

double tempmax = temp;

double ebar, evar, emin, eta, specific_heat; double delta;

double chi = 0.8; // Annealing schedule

double chi_s = 3.0; // Vanderbilt/Louie 'growth factor' double rm;

double root3 = sqrt(3.0);

double p = 0.02/sqrt(3.0); //max size of annealing step param_current = new double[nparam]; param_trial = new double[nparam]; cost_current = cost_in;

MPI_Comm_rank(MPI_COMM_WORLD, &rank); sprintf(fname, \ fp_log_file = fopen(fname, \

if (fp_log_file == (FILE *) NULL) errorMessage(\ // Work out the number of active parameters, and set up the // index table of the active parameters.

// Note that the complete array of parameters (param_trial) must // be used to evaluate the cost function.

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nactive = 0;

for (n = 0; n < nparam; n++) {

param_current[n] = param_in[n]; param_trial[n] = param_in[n]; if (P.is_active[n]) nactive++; }

param_index = new int[nactive]; i = 0;

for (n = 0; n < nparam; n++) {

if (P.is_active[n]) param_index[i++] = n; }

// Initialise the step distribution matrix Q_ij Q = new double[nactive*nactive]; S = new double[nactive*nactive]; u = new double[nactive]; dp = new double[nactive]; A = new double[nactive]; double * Qtmp;

Qtmp = new double[nactive*nactive]; for (i = 0; i < nactive; i++) { for (j = 0; j < nactive; j++) { delta = (i == j);

Q[i*nactive + j] = p*delta*param_current[param_index[j]]; } }

// carry out annealing points nblock = nstep/nstep_per_block; rm = 1.0/(double) nstep_per_block; for (block = 0; block < nblock; block++) {

// Set the schedule for this block, and initialise blockwise quantities. // We also ensure the step distribution matrix is diagonal. temp = chi*temp;

for (i = 0; i < nactive; i++) { A[i] = 0.0;

for (j = 0; j < nactive; j++) { S[i*nactive + j] = 0.0; delta = (i == j);

Q[i*nactive + j] *= delta; } }

ebar = 0.0; evar = 0.0;

emin = cost_current;

for (i = 0; i < nactive; i++) {

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printf(\ }

for (step = 0; step < nstep_per_block; step++) {

// Set the random vector u, and compute the step size dp for (i = 0; i < nactive; i++) { u[i] = root3*(r_uniform()*2.0 - 1.0); }

for (i = 0; i < nactive; i++) { dp[i] = 0.0;

for (j = 0; j < nactive; j++) {

dp[i] += Q[i*nactive + j]*u[j]; }

}

for (i = 0; i < nactive; i++) { n = param_index[i];

param_trial[n] = param_current[n] + dp[i];

if (param_trial[n] < P.min[n]) param_trial[n] = P.min[n]; if (param_trial[n] > P.max[n]) param_trial[n] = P.max[n]; }

// calculate new cost function score p_model->setParameters(param_trial); cost_trial = p_costWild->getCost(); cost_trial += p_costLHY->getCost(); cost_trial += p_costTOC1->getCost(); cost_trial += p_costAPRR->getCost(); // Metropolis

delta = cost_trial - cost_current;

if (delta < 0.0 || r_uniform() < exp(-delta/temp)) { for (n = 0; n < nparam; n++) {

param_current[n] = param_trial[n]; }

cost_current = cost_trial; ++n_accepted; }

// 'Energy' statistics ebar += cost_current;

evar += cost_current*cost_current;

if (cost_current < emin) emin = cost_current; // Per time step log

fprintf(fp_log_file, \ block, step, temp,

cost_current, cost_trial,

(float) n_accepted / (float) (block*nstep_per_block + step)); // Accumulate average, measured covariance

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for (i = 0; i < nactive; i++) {

A[i] += param_current[param_index[i]]; for (j = 0; j < nactive; j++) { S[i*nactive + j]

+= param_current[param_index[i]]*param_current[param_index[j]]; }

}

/* Next step*/ }

// Set the previous block average and measured covariance for (i = 0; i < nactive; i++) { A[i] = rm*A[i]; }

for (i = 0; i < nactive; i++) { for (j = 0; j < nactive; j++) {

S[i*nactive + j] = rm*S[i*nactive + j] - A[i]*A[j];

if (i == j) printf(\ // Set the convarience for the next iteration s = 6 chi_s S / M S[i*nactive + j] = 6.0*chi_s*rm*S[i*nactive + j]; } }

// Reset the step distribution matrix for the next block i = do_cholesky(nactive, S, Q); j = test_cholesky(nactive, S, Q); printf(\ // Block statistics ebar = rm*ebar; evar = rm*evar;

specific_heat = (evar - ebar*ebar) / temp*temp; eta = (ebar - emin)/ebar;

fprintf(fp_log_file, \ block, nstep_per_block, temp, ebar, evar, emin, specific_heat, eta); /* Next block */ }

*cost_out = cost_current; for (n = 0; n < nparam; n++) {

params_out[n] = param_current[n]; }

fclose(fp_log_file); delete param_index; delete param_current; delete param_trial; delete Q;

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delete u; delete dp; delete S; delete A; return;

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