Abstract:
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One of the basic operations in communication networks consists in establishing routes
for connection requests between physically separated network nodes. In many situations,
either due to technical constraints or to quality-of-service and survivability requirements, it is
required that no two routes interfere with each other. These requirements apply in particular
to routing and admission control in large-scale, high-speed and optical networks. The same
requirements also arise in a multitude of other applications such as real-time communications,
vlsi design, scheduling, bin packing, and load balancing. This problem can be modeled as
a combinatorial optimization problem as follows. Given a graph G representing a network
topology, and a collection T = f(s1; t1) : : : (sk; tk)g of pairs of vertices in G representing
connection request, the maximum edge-disjoint paths problem is an NP-hard problem that
consists in determining the maximum number of pairs in T that can be routed in G by
mutually edge-disjoint si - ti paths.
We propose an ant colony optimization (aco) algorithm to solve this problem. aco algo-
rithms are approximate algorithms that are inspired by the foraging behavior of real ants. The
decentralized nature of these algorithms makes them suitable for the application to problems
arising in large-scale environments. First, we propose a basic version of our algorithm in order
to outline its main features. In a subsequent step we propose several extensions of the basic
algorithm and we conduct an extensive parameter tuning in order to show the usefulness of
those extensions. In comparison to a multi-start greedy approach, our algorithm generates
in general solutions of higher quality in a shorter amount of time. In particular the run-time
behaviour of our algorithm is one of its important advantages. |