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###################################################### ########## ACO algorithms for the TSP ########## ######################################################

  Version: 1.0
  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.c (you only need this one) timer.h (and this one) dos_timer.c (same as timer.c) dos_timer.h (same as timer.c) unix_timer.c (in case you want to use rusage instead, use this one) unix_timer.h (in case you want to use rusage instead, use this one)

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, the webpage for the 8th DIMACS Implementation Challenge on the TSP (http://www.research.att.com/~dsj/chtsp/) or the webpage on "Solving TSP"

===== 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 -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 <trial_number>

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 <trial_number>

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 informatik.tu-darmstadt.de