void
kdtree_semigreedy_tour(kdtree *tree, data *d, int start, int *tour, int *len)
{
int dim = d->numnodes;
int i, current, next;
int lenght = 0;
/* RCL. */
int j, k;
double total_bias, prob_acc, prob_pick, prob;
struct rcl params;
params.candidates = MALLOC(dim, struct heapelm);
tour[0] = start;
current = start;
for (i = 1; i < dim; i++) {
kdtree_delete(tree, current);
/* Define RCL size. */
params.rcl_used = 0;
params.rcl_size = RANDOM(3) + 1;
kdtree_m_nearest_neighbours(tree, d, current, ¶ms);
/*printf("RCL size: %d, used: %d\n", params.rcl_size, params.rcl_used);
heap_dump(params.candidates, params.rcl_used);*/
/* Pick an element from RCL. */
total_bias = 0;
for (j = 1; j <= params.rcl_used; j++) {
total_bias += BIAS(j);
}
prob_acc = 0;
prob_pick = RANDOM_UNIT();
for (j = params.rcl_used, k = 1; j >= 0; j--, k++) {
prob = BIAS(k) / total_bias;
if (prob + prob_acc > prob_pick) {
break;
}
prob_acc += prob;
}
/*printf("Picked %d: %d, %d\n", j, params.candidates[j - 1].num,
params.candidates[j - 1].cost);*/
next = params.candidates[j - 1].num;
/* Update tour. */
tour[i] = next;
lenght += params.candidates[j - 1].cost;
current = next;
}
lenght += d->dist(d, current, start);
*len = lenght;
kdtree_undelete_all(tree, d->numnodes);
free(params.candidates);
}
static int probabilistic_choice (double *probs, int size, double div) {/*{{{*/
double p, last;
int i;
p = (double) RANDOM_UNIT() / INT_MAX;
last = 0;
for (i = 0; i < size; i++) {
if (p <= last + (probs[i]/div)) {
return i;
}
last += (probs[i]/div);
}
return i;
}/*}}}*/
static int choose_vertex(double *probb, int size) {/*{{{*/
double p, last, div;
int i;
div = probb[size];
p = (double) RANDOM_UNIT() / INT_MAX;
last = 0;
for (i = 0; i < size; i++) {
if (p <= last + (probb[i]/div)) {
return i;
}
last += (probb[i]/div);
}
abort();
}/*}}}*/
/* lct: location
* flt: facility */
int next_facility(QAP_t *p, set_t *s, int lct){
/*
int fcl;
int x = rand() % s->size;
fcl = s->data[x];
s->data[x] = s->data[s->size - 1];
s->size--;
return fcl;
*/
double delta, x, prob[N], last;
int flt, i;
delta = 0;
for (i = 0; i < p->size; i++) {
delta += pheromone[lct][i];
}
for (i = 0; i < s->size; i++) {
flt = s->data[i];
prob[i] = pheromone[lct][flt] / delta;
}
x = (double) RANDOM_UNIT() / INT_MAX;
last = 0;
for (i = 0; i < s->size; i++) {
if (x <= last + prob[i]) {
flt = s->data[i];
break;
}
last += prob[i];
}
s->data[i] = s->data[s->size - 1];
s->size--;
return flt;
}
static void
semi_greedy(data *d, int start, int *tour, int *tour_length)
{
int i, j, k;
int dim = d->numnodes;
int current, len, dist;
int *visited = MALLOC(dim, int);
/* RCL. */
struct heapelm *candidates = MALLOC(dim, struct heapelm);
int rclsize, rclused;
double total_bias, rprob, racc, rpick;
memset(visited, 0, dim * sizeof(int));
len = 0;
current = start;
for (i = 0; i < dim - 1; i++) {
visited[current] = 1;
tour[i] = current;
/* Define RCL size. */
rclused = 0;
if (RANDOM_UNIT() < 0.05) rclsize = dim - i - 2;
else rclsize = RANDOM(((dim - i - 2) / 4) + 1);
rclsize++;
DEBUG("RCL size: %d\n", rclsize);
/* Define RCL. */
for (j = 0; j < dim; j++) {
if (!visited[j]) {
dist = d->dist(d, current, j);
if (rclused < rclsize) {
candidates[rclused].num = j;
candidates[rclused].cost = dist;
rclused++;
if (rclused == rclsize) {
heap_build_max(candidates, rclsize);
}
} else if (dist < candidates[0].cost) {
heap_replace_root(candidates, rclsize, j, dist);
}
}
}
DEBUG("RCL used: %d\n", rclused);
heap_dump(candidates, rclused);
/* Pick RCL element based on logarithmic bias. */
#define BIAS_LOG(r) (1/log((r)+1))
#define BIAS_LINEAR(r) (1./(r))
#define BIAS_EXP(r) (1/exp(r))
#define BIAS_RANDOM(r) 1
#define BIAS(r) BIAS_LOG(r)
total_bias = 0;
for (j = 1; j <= rclused; j++) {
total_bias += BIAS(j);
}
racc = 0;
rpick = RANDOM_UNIT();
#if DEBUGGING
for (j = rclused; j >= 1; j--) {
DEBUG("%f ", BIAS(j) / total_bias);
}
DEBUG("\nR: %f\n", rpick);
#endif
for (j = rclused, k = 1; j >= 0; j--, k++) {
rprob = BIAS(k) / total_bias;
if (rprob + racc > rpick) {
break;
}
racc += rprob;
}
DEBUG("Picked: j = %d, %d %d\n\n", j,
candidates[j-1].num, candidates[j-1].cost);
current = candidates[j - 1].num;
len += candidates[j - 1].cost;
}
tour[i] = current;
len += d->dist(d, current, start);
*tour_length = len;
free(visited);
free(candidates);
#undef BIAS
#undef BIAS_LOG
}