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acoqap.c
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acoqap.c
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/*
AAAA CCCC OOOO QQQQ AAAA PPPPP
AA AA CC OO OO QQ QQ AA AA PP PP
AAAAAA CC OO OO QQ QQ AA AA PPPPP
AA AA CC OO OO QQ QQ AA AA PP
AA AA CCCC OOOO QQQQQ AA AA PP
Q
######################################################
########## ACO algorithms for the QAP ##########
######################################################
Version: 1.0
File: acoqap.c
Author: Thomas Stuetzle, Manuel Lopez-Ibanez
Purpose: QAP specific routines for the ACOQAP algorithms
Check: README and gpl.txt
Copyright (C) 1997, 2015 Thomas Stuetzle, Manuel Lopez-Ibanez
*/
/***************************************************************************
Program's name: acoqap
Ant Colony Optimization algorithms (AS, ACS, EAS, RAS, MMAS, BWAS) for the QAP
Copyright (C) 2004 Thomas Stuetzle
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
email: stuetzle no@spam ulb.ac.be
mail address: Universite libre de Bruxelles
IRIDIA, CP 194/6
Av. F. Roosevelt 50
B-1050 Brussels
Belgium
***************************************************************************/
#include <stdio.h>
#include <math.h>
#include <limits.h>
#include <assert.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>
#include "ants.h"
#include "utilities.h"
#include "InOut.h"
#include "QAP.h"
#include "timer.h"
#include "aco.h"
#include "qap-ls.h"
#include "adaptation.h"
const char * const PROG_ID_STR = "ACO algorithms for the QAP";
void problem_set_default_ls_parameters(void)
{
dlb_flag = FALSE; /* don't apply don't look bits in local search */
nn_ls = 1; /* unused in the QAP */
nn_ants = 0; /* no candidate list */
}
/* These override any algorithm-specific settings. */
void problem_set_default_parameters(void)
{
ls_flag = LS_best_2_opt;
nn_ls = 1; /* unused in the QAP */
nn_ants = 0; /* no candidate list */
opt_n_ants = 5;
opt_rho = 0.2; /* Default in the published paper on MMAS for the
QAP */
opt_beta = 0.0;
p_dec = 0.005;
schedule_length = 20;
min_iters_after_restart_best = 5;
restart_freq = 1;
restart_branch_factor = 1.1;
}
void construct_solutions( void )
/*
FUNCTION: manage the solution construction phase
INPUT: none
OUTPUT: none
(SIDE)EFFECTS: when finished, all ants of the colony have constructed a solution
*/
{
long int k; /* counter variable for ants */
long int j; /* counter variable */
long int *objects;
trace_print("construct solutions for all ants\n");
/* Different from the TSP, here ants construct solutions sequentially. */
for ( k = 0 ; k < n_ants ; k++ ) {
trace_print("construct solution for ant %ld\n",k);
/* free ants for next construction */
ant_empty_memory( &ant[k] );
/* construct for one ant after another (sequential) */
/* random order of objects to be assigned to locations */
objects = generate_random_permutation( n );
for ( j = 0 ; j < n ; j++ ) {
choose_and_move_to_next( &ant[k], objects[j] );
if ( acs_flag )
local_acs_pheromone_update( &ant[k], j );
}
ant[k].tour_length = compute_tour_length( ant[k].tour );
checkTour( ant[k].tour );
free(objects);
n_tours++;
}
trace_print("end construction\n");
}
double node_branching(double l)
/*
FUNCTION: compute the average node lambda-branching factor
INPUT: lambda value
OUTPUT: average node branching factor
(SIDE)EFFECTS: none
COMMENTS: see the ACO book for a definition of the average node
lambda-branching factor
*/
{
long int i, m;
double min, max, cutoff;
double avg;
double *num_branches;
num_branches = calloc(n, sizeof(double));
for ( m = 0 ; m < n ; m++ ) {
/* determine max, min to calculate the cutoff value */
min = pheromone[m][0];
max = min;
for ( i = 1 ; i < n ; i++ ) {
if ( pheromone[m][i] > max )
max = pheromone[m][i];
else if ( pheromone[m][i] < min )
min = pheromone[m][i];
}
cutoff = min + l * (max - min);
for ( i = 0 ; i < n ; i++ ) {
if ( pheromone[m][i] > cutoff )
num_branches[m]++;
}
}
avg = 0.;
for ( m = 0 ; m < n ; m++ ) {
avg += num_branches[m];
}
free ( num_branches );
/* Norm branching factor to minimal value 1 */
return ( avg / (double) n );
}
/* The convergence factor gives an indication about how far the algorithm is
from convergence.
C. Blum and M. Dorigo. 2004. The Hyper-Cube Framework for Ant Colony
Optimization. IEEE Transactions on Systems, Man and Cybernetics, Part B
(Cybernetics) 34 (2): 1161-72.
*/
double compute_convergence_factor()
{
double cf = 0.0;
long int i,j;
for (i = 0; i < n; i++) {
for (j = 0; j < n; j++) {
cf += MAX (trail_max - pheromone[i][j],
pheromone[i][j] - trail_min);
}
}
cf /= (n * n * (trail_max - trail_min));
cf = 2.0 * (cf - 0.5);
return cf;
}
void mmas_update( void )
/*
FUNCTION: manage global pheromone deposit for MAX-MIN Ant System
INPUT: none
OUTPUT: none
(SIDE)EFFECTS: either the iteration-best or the best-so-far ant deposit pheromone
on matrix "pheromone"
*/
{
/* we use default upper pheromone trail limit for MMAS and hence we
do not have to worry regarding keeping the upper limit */
long int iterations_since_restart = iteration - restart_iteration;
long int iteration_best_ant;
trace_print("MAX-MIN Ant System pheromone deposit\n");
/* This is what the old MMASQAP code did : */
if ( iterations_since_restart < 5 ) {
iteration_best_ant = find_best();
global_update_pheromone( &ant[iteration_best_ant] );
trace_print("pheromone update: iteration-best: iteration_since_restart = %ld\n",
iterations_since_restart);
}
/* ??? Thus, it doesn't make sense that schedule_length is < 5 */
else if ( iterations_since_restart < schedule_length ) {
if (iterations_since_restart % u_gb) {
iteration_best_ant = find_best();
global_update_pheromone( &ant[iteration_best_ant] );
trace_print("pheromone update: iteration-best: "
"iteration_since_restart = %ld, u_gb = %ld\n",
iterations_since_restart, u_gb);
} else {
global_update_pheromone( restart_best_ant );
trace_print("pheromone update: restart-best: "
"iteration_since_restart = %ld, u_gb = %ld\n",
iterations_since_restart, u_gb);
}
} else {
global_update_pheromone( best_so_far_ant );
trace_print("pheromone update: global-best: "
"iteration_since_restart = %ld\n",
iterations_since_restart);
}
if (ls_flag == LS_tabu_search_short || ls_flag == LS_tabu_search_long) {
if ( iterations_since_restart < schedule_length )
u_gb = 2;
else
u_gb = 1;
}
else if ( iterations_since_restart > schedule_length )
u_gb = 1;
else if ( iterations_since_restart > schedule_length / 2)
u_gb = 2;
else
u_gb = 3;
}