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Finding Edge-disjoint Paths in Networks: An Ant Colony Optimization Algorithm

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Journal of Mathematical Modelling and Algorithms

Abstract

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={(s 1,t 1)...(s k ,t k )} 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 s i −t i paths. We propose an ant colony optimization (aco) algorithm to solve this problem. aco algorithms 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.

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Abbreviations

EDP:

(maximum) edge-disjoint paths problem

SGA:

Simple Greedy Algorithm

MSGA:

Multi-start Greedy Algorithm

ACS:

Ant Colony System

ACO:

Ant Colony Optimization

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Authors and Affiliations

  1. ALBCOM research group, Dept. Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Jordi Girona 1–3, Ω building Campus Nord, E-08034, Barcelona, Spain

    Maria J. Blesa & Christian Blum

Authors
  1. Maria J. Blesa
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  2. Christian Blum
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Corresponding author

Correspondence to Christian Blum.

Additional information

Work partially supported by the fet Integrated Project 15964 (aeolus), and by the Spanish cicyt projects tin2005-09198-c02-02 (asce), tin2005-08818-c04-02 (oplink) and tin2005-25859-e. C. Blum also acknowledges support by the ramón y cajal postdoctoral program of the Spanish Ministry of Science and Technology. Preliminary versions of this work were presented at the 1st European Workshop on Evolutionary Computation in Communications, Networks, and Connected Systems, lncs 3005:160–169, Springer 2004, and in the 9th Intl. Workshop on Nature Inspired Distributed Computing, p. 239, ieee 2006.

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Blesa, M.J., Blum, C. Finding Edge-disjoint Paths in Networks: An Ant Colony Optimization Algorithm. J Math Model Algor 6, 361–391 (2007). https://doi.org/10.1007/s10852-007-9060-y

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  • DOI: https://doi.org/10.1007/s10852-007-9060-y

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