swarm info

, ACO algorithms have been tested on a large number of
academic problems. These include problems related to the traveling salesman, as well
as assignment, scheduling, subset, and constraint satisfaction problems. For many of
these, world-class performance has been achieved. For example, ACO algorithms
are, at the time of writing, state-of-the-art (i.e., their performance is comparable to,
or better than, that of the best existing methods other than ACO) for the sequential
ordering problem (Gambardella & Dorigo, 2000), the vehicle routing problem with
time window constraints (Gambardella et al., 1999), the quadratic assignment problem
(Maniezzo, 1999; Stu¨ tzle & Hoos, 2000), the group shop scheduling problem
(Blum, 2003a), the arc-weighted l-cardinality tree problem (Blum & Blesa, 2003), and
the shortest common supersequence problem (Michel & Middendorf, 1999). Additionally,
very good performance has been obtained by AntNet (see chapter 6) on
network routing problems (Di Caro & Dorigo, 1998c).
This success with academic problems has raised the attention of a number of
companies that have started to use ACO algorithms for real-world applications.
Among the first to exploit algorithms based on the ACO metaheuristic is EuroBios
(www.eurobios.com). They have applied ACO to a number of di¤erent scheduling