Artificial Bee Colony for optimization of cloud-ready and survivable elastic optical networks

Goścień, Róża; Lozano, Manuel

doi:10.1016/j.comcom.2018.07.011

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…Because of its fewer control parameters and outstanding exploration ability, the ABC algorithm is used in many other fields containing the 0-1 knapsack problem [14], traveling salesmen problem (TSP) [15], [16], the path planning approach [17], the scheduling problem [18], the data clustering problem [19], the hybrid classification [20] and training the feedforward neural network model [21]. In the other research fields, the ABC algorithm and its modified versions deal with the optimization problem in feature selection [22], automatic text summarization method [23], reservoir system operation [24], blocking lot-streaming flow shop (BLSFS) scheduling problem [25], automatic clustering for customer segmentation [26], cloud-ready and survivable elastic optical networks [27], the design of individual tourist routes [28], dynamic green bike repositioning problem [29] and movie recommender system [30].…”

Section: Related Workmentioning

confidence: 99%

Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization

et al. 2019

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With the development of science and technology, the accuracy requirements for solving engineering problems are getting stricter than before. Most structural design optimization problems in civil and mechanical engineering have proven to be the non-deterministic polynomial hard problems. The artificial bee colony (ABC) algorithm has been proven to be an effective method of design optimization problems. This paper proposes an improved ABC algorithm (DSM-ABC) combined with dual-search mechanism containing Lévy flight and differential self-perturbation and applies it to three classical structural design problems, including cantilever beam design, gear train design, and three-bar truss design. The experimental results of benchmark functions from CEC2005 reveal that the proposed DSM-ABC algorithm accelerates the convergence and improves the performance. Eventually, the obtained results of optimization structural design problems prove that the DSM-ABC algorithm has a strong superiority compared with the state-ofthe-art algorithms in solving optimization engineering design problems.INDEX TERMS Artificial bee colony algorithm, dual search mechanism, Lévy flight, differential selfdisturbance mechanism, engineering optimization.

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Section: Related Workmentioning

confidence: 99%

Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization

et al. 2019

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“…For the Elastic Optical Network (EON) flow allocation problem the work [26] proposes two optimization-based metaheuristics, one by the particle swarm algorithm and the other by the taboo search and is represented by ILP. In the paper [27] it is proposed an optimization method based on the Artificial Bee Colony algorithm (ABC) modeled by ILP.…”

Section: Introductionmentioning

confidence: 99%

“…This paper aims to develop a decision support system to be used in the strategic planning of optical transport networks. As in [23] [24][25] [26] [27], it is also considered the use of ILP in this work. More specifically, we propose an integer linear programming model 0-1 to model the optical transport network, which is solved using the exact method, the bio-inspired genetic and firefly algorithms and a Hybrid Firefly-Genetic Algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The dimensioning of the resources is performed aiming at a minimum cost, that is related to the possibilities of transmission equipment allocation in the nodes and allocation of optical link and regenerators in the network links. Unlike the approaches proposed in [23] [25] [26] [27], one of the remarkable characteristics of our proposal is the dimensioning of the network through a process of efficient resource allocation plus the possibility of draining demands considering multiple overflow nodes.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Firefly-Genetic Algorithm for Planning of Optical Transport Networks

Oliveira

Sousa²,

Vieira³

2020

JCIS

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The network design is a well-known combinatorial optimization problem that is classified as NP-Hard. The main purpose of design is often the same, to allocate and size available resources in the most possible efficient way in terms of budget, considering optimization models oriented towards minimizing costs. In this paper, it is proposed to use a hybrid optimization method called Hybrid Firefly-Genetic Algorithm to solve the Integer Linear Programming (ILP) problem, for Optical Transport Network (OTN) planning, considering cost minimization. The method combines the discrete firefly algorithm with the standard genetic algorithm. To apply this method, first it is proposed a mathematical formulation for the optical network planning problem. Two other relevant novelties of the proposed model are: 1-network modeling that considers a traffic interest matrix in which each predicted demand can be drained from its generating node (origin) to one of the possible network overflow nodes (different destination nodes); this modeling allows the dimensioning of a transport network capable of serving different service profiles at each of its nodes; 2-network resources that are efficiently allocated with determination of the capacity of each transmission system according to the best solutions for the traffic flow links. This allocation process allows the model to value those solutions with better resource sharing to the detriment of the use of several parallel links operating opportunely with idleness in each one. The results show that the proposed algorithm outperforms other metaheuristics described in the literature.

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“…On the other hand, using heuristics based on SI [38,39] or LA [9], complexity becomes polynomial, e.g., O(N 3 ), so development and deployment become practically feasible for the majority of large network topologies. For example, this issue is confronted in the basic ACO outline [36] which shows that complexity is not a hurdle for implementation and deployment.…”

Section: Methods For Reducing Complexitymentioning

confidence: 99%

Intelligent protocols for access and backbone networks

Κυριακόπουλος¹

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Τα οπτικά δίκτυα υπολογιστών παρέχουν τα μέσα για αξιόπιστες επικοινωνίες δεδομένων με χαμηλή καθυστέρηση μετάδοσης και διάδοσης, χαμηλό ρυθμό σφαλμάτων και συγχρόνως, υψηλή ρυθμαπόδοση. Ο ρυθμός υιοθέτησής τους σταθερά αυξάνεται για την εκπλήρωση των αναγκών συνδεσιμότητας στον δικτυακό κορμό μεγάλων χωρών ή τη σταθερή ανάγκη για πρόσβαση από τη σκοπιά του τελικού χρήστη. Η αύξηση της χρήσης πόρων οδηγεί σε υψηλότερη ενεργειακή κατανάλωση, η οποία αποτελεί παράγοντα για αντιμετώπιση, ενσωματώνοντας την ενεργειακή αποδοτικότητα στην υπολογιστική λογική καθώς μεταφέρονται τα δεδομένα. Οι ενεργειακά αποδοτικοί αλγόριθμοι σχεδιάζουν τη λογική τοπολογία κορμού του δικτύου με χαμηλή κατανάλωση ενέργειας και τη διατήρηση της απόδοσης σε υψηλό επίπεδο. Από την άλλη πλευρά, τα οπτικά δίκτυα πρόσβασης μεσολαβούν ανάμεσα στους χρήστες και το δίκτυο κορμού, καλύπτοντας το τελευταίο μίλι της συνδεσιμότητας. Η γρήγορη πρόσβαση σε αυτά τα δίκτυα από τη σκοπιά του τελικού χρήστη, βελτιώνει την ποιότητα των υπηρεσιών τους και τη συνολική ρυθμαπόδοση. Οι τεχνητά νοήμονες αλγόριθμοι εκμεταλλεύονται την κατάσταση λειτουργίας του δικτύου με προσαρμογή στις συνθήκες της, εναποθέτοντας πόρους πιο αποδοτικά συγκριτικά με τις στατικές εναλλακτικές. Οπότε, όντας κατάλληλοι είτε για τον κορμό, είτε για το μέρος πρόσβασης του δικτύου. Δύο τύποι με τεχνητά νοήμονες αλγορίθμους έχουν σχεδιαστεί και αξιολογηθεί, οι οποίοι είναι ικανοί για την επίτευξη τον προαναφερθέντων στόχων, σχετιζόμενοι με ενεργειακή αποδοτικότητα στην υπολογιστική λογική για το δίκτυο κορμού. Επιπλέον, αυτοί που προσφέρουν προσαρμοστική κατανομή του εύρους ζώνης στην πλευρά πρόσβασης του δικτύου.

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Artificial Bee Colony for optimization of cloud-ready and survivable elastic optical networks

Cited by 12 publications

References 23 publications

Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization

Dual-Search Artificial Bee Colony Algorithm for Engineering Optimization

A Hybrid Firefly-Genetic Algorithm for Planning of Optical Transport Networks

Intelligent protocols for access and backbone networks

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