A genetic algorithm for the maximum edge-disjoint paths problem

Optimization problems concerning edge-disjoint paths have attracted considerable attention for decades. These problems have a lot of applications in the areas of call admission control, real-time communication, VLSI (Very-large-scale integration) layout and reconfiguration, packing, etc. The maximum...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 148; pp. 17 - 22
Main Authors Hsu, Chia-Chun, Cho, Hsun-Jung
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier B.V 19.01.2015
Elsevier
Subjects
Ant colony optimization
Applied sciences
Computer science; control theory; systems
Disjoint paths
Edge-disjoint paths
Exact sciences and technology
Genetic algorithm
Information retrieval. Graph
MEDP
Theoretical computing
MEDP
Ant colony optimization
Edge-disjoint paths
Genetic algorithm
Disjoint paths
Disjoint edge
Routing
NP hard problem
Greedy algorithm
Swarm intelligence
Cardiology
Non directed graph
Mathematical programming
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Summary:Optimization problems concerning edge-disjoint paths have attracted considerable attention for decades. These problems have a lot of applications in the areas of call admission control, real-time communication, VLSI (Very-large-scale integration) layout and reconfiguration, packing, etc. The maximum edge-disjoint paths problem (MEDP) seems to lie in the heart of these problems. Given an undirected graph G and a set of I connection requests, each request consists of a pair of nodes, MEDP is an NP-hard problem which determines the maximum number of accepted requests that can be routed by mutually edge-disjoint (si,ti) paths. We propose a genetic algorithm (GA) to solve the problem. In comparison to the multi-start simple greedy algorithm (MSGA) and the ant colony optimization method (ACO), the proposed GA method performs better in most of the instances in terms of solution quality and time.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.10.046