Particle Swarm Optimization of network migration planning

Türk, Stefan; Liu, Xiaoyan; Radeke, Rico; Lehnert, Ralf

doi:10.1109/glocom.2013.6831406

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…We also observe that the algorithm suggests migrations in the beginning of the migration window, in order to get more revenue from the subscribers. This behaviour is also recorded by the heuristic optimization models of [11].…”

Section: A Pure Residential Migrationmentioning

confidence: 94%

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Rational Agent-Based Decision Algorithm for Strategic Converged Network Migration Planning

Patri¹,

Grigoreva²,

Kellerer³

et al. 2019

J. Opt. Commun. Netw.

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To keep up with constantly growing user demands for services with higher quality and bandwidth requirements, telecommunication operators are forced to upgrade their networks. This upgrade, or migration of the network to a new technology, is a complex strategic network planning problem that involves techno-economic evaluations over multiple periods of time. The state-of-the-art approaches consider migrations to a concrete architecture and do not take uncertainties, such as user churn, into account. This results in migration cost underestimations and profitability over-estimations. In this paper, we propose a generic migration algorithm derived from a search based rational agent decision process that can deal with uncertainties and provides the migration path using a maximized utility function. The algorithm maximizes the migration project profitability, measured as accumulated Net Present Value (NPV). This flexible and generic methodology has been evaluated on the example of migration from existing copper networks to the future-proof Passive Optical Network (PON) architectures. Our proposed flexible migration algorithm is validated over pure residential and converged scenarios in a fully reproducible case study. The results yeld that the migration flexibility is a key to the profit maximization.

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Section: A Pure Residential Migrationmentioning

confidence: 94%

“…The use of AI based heuristic search in access network migration planning is studied in the work done by [11] and [12]. The problem statement defined in that work is to optimize the migration process from VDSL to FTTC GPON in an urban access network, undertaken in a pre-defined migration period.…”

Section: State Of the Artmentioning

confidence: 99%

Rational Agent-Based Decision Algorithm for Strategic Converged Network Migration Planning

Patri¹,

Grigoreva²,

Kellerer³

et al. 2019

J. Opt. Commun. Netw.

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show abstract

“…] proposed an intelligent fire evacuation system based on Ant Colony Optimization and MapX.The software calculated shortest path by fire points and ant colony optimization and updated lamp status on floor.Junaedi and etal[6] proposed an evacuation plan in case of fire break by employing multi agent behavior and…”

mentioning

confidence: 99%

Evacuation Plans and Simulations for Crowd Egress – A Review

Goyal¹,

Saxena²

2018

IJAEMS

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Due to growing population density and increasing complexity and congestion of human habitat evacuation planning is essential to avoid major loss of life during a natural or un-natural disaster. The paper provides a review of existing evacuation systems and strategies and also points out the possible research directions. Intelligent Evacuation Management System coupled with evolutionary and machine learning techniques such as PSO,ACO , k -mean clustering is a promising solution to ensure safe and jostle free egress of people. .

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Network migration optimization using meta-heuristics

Türk

2014

AEU - International Journal of Electronics and Communications

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Particle Swarm Optimization of network migration planning

Cited by 3 publications

References 10 publications

Rational Agent-Based Decision Algorithm for Strategic Converged Network Migration Planning

Rational Agent-Based Decision Algorithm for Strategic Converged Network Migration Planning

Evacuation Plans and Simulations for Crowd Egress – A Review

Network migration optimization using meta-heuristics

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