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https://github.com/mlopez-ibanez/acotspqap
ACOTSPQAP: Ant Colony Optimization Algorithms for the Travelling Salesman Problem and the Quadratic Assignment Problem
https://github.com/mlopez-ibanez/acotspqap
metaheuristic optimization qaplib tsp-solver
Last synced: 12 days ago
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ACOTSPQAP: Ant Colony Optimization Algorithms for the Travelling Salesman Problem and the Quadratic Assignment Problem
- Host: GitHub
- URL: https://github.com/mlopez-ibanez/acotspqap
- Owner: MLopez-Ibanez
- Created: 2020-05-22T15:22:02.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-03-09T14:15:24.000Z (almost 2 years ago)
- Last Synced: 2024-11-12T04:40:24.728Z (about 2 months ago)
- Topics: metaheuristic, optimization, qaplib, tsp-solver
- Language: C
- Size: 1.67 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README-ACOTSP.txt
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README
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######################################################
########## ACO algorithms for the TSP ##########
######################################################
Version: 1.05
Author: Thomas Stuetzle
Copyright (c) Thomas Stuetzle, 2002
This is the README file to the software package ACOTSP.
This software package was developed by Thomas Stuetzle in connection
with the Book
[DorStu04] Marco Dorigo and Thomas Stuetzle, "Ant Colony
Optimization", MIT Press, Cambridge, MA, USA, 2004.
The software package is freely available subject to the
GNU General Public Licence, which is included in file gpl.txt.
If you use ACOTSP in your research, I would appreciate a citation in
your publication(s). Please cite it as
Thomas Stuetzle. ACOTSP, Version 1.0. Available from
http://www.aco-metaheuristic.org/aco-code, 2004.
This software package provides an implementation of various Ant Colony
Optimization (ACO) algorithms for the symmetric Traveling
Salesman Problem (TSP). The ACO algorithms implemented are Ant System,
Elitist Ant System, MAX-MIN Ant System, Rank-based version of Ant
System, Best-Worst Ant System, and Ant Colony System. This is Version
1.0 of ACOTSP; it is in large part identical to the software used to
produce the results in [DorStu04], but it has been slightly adapted to
make the code more readable, more comments were added, and a new
command line parser was generated with opag.
AIMS OF THE SOFTWARE: This software was developed to have one common
code for the various known ACO algorithms that were at some point
applied to the TSP in the literature. The software tries to provide a
reasonably efficient implementation of these ACO algorithms while at
the same time aiming for readability and understandability of the
code.
=========
CONTENTS
=========
The GNU General Public Licence:
gpl.txt
The main control routines, main:
acotsp.c
Procedures to implement the ants behaviour:
ants.c
ants.h
Input / output / statistics routines:
InOut.c
InOut.h
Procedures specific to the TSP:
TSP.c
TSP.h
Local search procedures:
ls.c
ls.h
Additional useful / helping procedure:
utilities.c
utilities.h
Command line parser:
parse.c
parse.h
Time measurement:
timer.h
dos_timer.c : default timer implementation based on clock()
unix_timer.c : in case you want to use rusage() instead, edit the
Makefile to use this one or compile with 'make TIMER=unix'
Makefile
Instances: Some problem instances from TSPLIB: eil51.tsp kroA100.tsp
d198.tsp lin318.tsp pcb442.tsp att532.tsp rat783.tsp pcb1173.tsp
d1291.tsp pr2392.tsp. Other TSP instances are available from TSPLIB
(http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/index.html),
the webpage for the 8th DIMACS Implementation Challenge on the TSP
(http://dimacs.rutgers.edu/Challenges/TSP/) or the webpage on "The
Traveling Salesman Problem"
(http://www.math.uwaterloo.ca/tsp/index.html)
=====
Code
=====
The software was developed in ANSI C under Linux, using the GNU 2.95.3
gcc compiler and extensively tested in this environment. The software
is distributed as a gzipped tar file.
To install the code, first obtain the file ACOTSP.V1.0.tar.gz. Unzip
the file by typing
gunzip ACOTSP.V1.0.tar.gz
and then unpack it by typing
tar -xvf ACOTSP.V1.0.tar
The software will unpack in a new folder ACOTSP.V1.0
To compile it under Linux just type 'make' and the executable 'acotsp'
is produced.
Note: The code is written in ANSI C. Hence, the code should be
reasonable portable to other Operating Systems than Linux or Unix.
======
USAGE
======
Given the large number of ACO algorithms, also the number of command
line options is relatively large.
The default parameter settings are such, that MAX-MIN Ant System will
be run using a 3-opt local search, using alpha = 1, beta = 2, rho =
0.5 for a maximum of 10 seconds per each trial for 10 independent
trials. (guess who developed MAX-MIN Ant System ;-)
The executable 'acotsp' provides the following command line options
(given are the short and the long options):
-r, --tries # number of independent trials
-s, --tours # number of steps in each trial
-t, --time # maximum time for each trial
--seed # seed for the random number generator
-i, --tsplibfile f inputfile (TSPLIB format necessary)
-o, --optimum # stop if tour better or equal optimum is found
-m, --ants # number of ants
-g, --nnants # nearest neighbours in tour construction
-a, --alpha # alpha (influence of pheromone trails)
-b, --beta # beta (influence of heuristic information)
-e, --rho # rho: pheromone trail evaporation
-q, --q0 # q_0: prob. of best choice in tour construction
-c, --elitistants # number of elitist ants
-f, --rasranks # number of ranks in rank-based Ant System
-k, --nnls # No. of nearest neighbors for local search
-l, --localsearch 0: no local search 1: 2-opt 2: 2.5-opt 3: 3-opt
-d, --dlb 1 use don't look bits in local search
-u, --as apply basic Ant System
-v, --eas apply elitist Ant System
-w, --ras apply rank-based version of Ant System
-x, --mmas apply MAX-MIN ant system
-y, --bwas apply best-worst ant system
-z, --acs apply ant colony system
-h, --help display the help text and exit
Options -u --as, -v --eas, -w --ras, -x --mmas, -y --bwas, -z --acs,
-h, --help don't need arguments, while all the others do.
A Mandatory option is only the option "-i, --tsplibfile". Here, mandatory
means that without specifying this option, the program won't work,
since there is no input file.
All the other options take some default values. The default values for
these are:
-r, --tries : 10
-s, --tours : 100
-t, --time : 10 /* seconds */
-o, --optimum : 1
-m, --ants : 25
-g, --nnants : 20
-a, --alpha : 1
-b, --beta : 2
-e, --rho : 0.5
-q, --q0 : 0.0
-c, --elitistants : 100
-f, --rasranks : 6
-k, --nnls : 20
-l, --localsearch : 3 /* use 3-opt */
-d, --dlb : 1
-u, --as : 0
-v, --eas : 0
-w, --ras : 0
-x, --mmas : 1 /* apply MAX-MIN Ant System */
-y, --bwas : 0
-z, --acs : 0
The default settings imply that as default MAX-MIN Ant System is run
using a 3-opt local search procedure. Please note that these default
values do not really make sense for some of the algorithms (e.g.,
typically an evaporation of 0.2 is recommended vor MAX-MIN Ant
System); that is, for some of the algorithms the default parameter
settings lead to poor performance (an example is ACS). Hence, when you
use any of the ACO algorithms, make sure you set the appropriate
parameter values. Typically, one may want to adjust the parameters
-t, --time
-o, --optimum
-m, --ants
-b, --beta
-e, --rho
-q, --q0
-l, --localsearch
Note that only one option among -u --as, -v --eas, -w --ras,
-x --mmas, -y --bwas, -z --acs, is to be specified.
Examples for running an experiments are:
./acotsp -i lin318.tsp -v -t 60. -o 42029 -m 50 -b 5
or
./acotsp --tsplibfile lin318.tsp --acs --rho 0.1 --q0 0.95 --time 60. --optimum 42029 --ants 10
=======
OUTPUT
=======
Every experiment produces three files. These files are
best.tsplibfilename
cmp.tsplibfilename
stat.tsplibfilename
where tsplibfilename is the instance identifier of the instance under
solution.
The most important of these is the file "cmp.tsplibfilename". This
file starts with a specification of the parameter settings used to run
the experiment. The section with the comprehensive experimental data
starts with
begin problem tsplibfilename
Next the random number seed for the next trial is given
Then, for each trial statistical information on the development of the
best-so-far solution is given. Each section for a trial starts with
begin try
Then, each time the algorithm finds a new best solution a line
best iteration tours time
is added, where "best" is the tour length of the best-so-far solution;
iteration is the iteration number in which this solution is found;
tours is the number of solutions constructed so far (typically this is
simple iteration X n_ants); and time is the time at which a new
best-so-far solution is found
Each trial is ended by
end try
Once all trials are run the line
end problem tsplibfilename
is added to end the file.
The file best.tsplibfilename
collects the information about parameter settings, the best solution
found in each trial, and some additional statistical information.
The file stat.tsplibfilename
may be used for the output of statistical information on a trial as
generated by the procedure population_statistics(); in InOut.c;
however, it is not heavily used in ACOTSP V1.0.
Have fun, and if you have any comments please write to
stuetzle no@spam ulb.ac.be