Bee Colony Optimization - part II: The application survey

Teodorović, Dušan; Šelmić, Milica; Davidović, Tatjana

doi:10.2298/yjor131029020t

Cited by 23 publications

(23 citation statements)

References 35 publications

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“…BCO metaheuristic is a population based stochastic random-search technique that is inspired by the foraging habits of natural bees looking for nectar sources [8][9][10][11][12][13][14][15] . It is originally proposed by Teodorović and Lučić in Ref.…”

Section: Bco Main Principlesmentioning

confidence: 99%

“…In this way, bees progressively discover solutions that are more promising and gradually leave worse ones. There are two approaches of the BCO metaheuristic 10,11 : the constructive BCO and the improvement (BCOi) versions. The first approach is based on constructive steps in which bees begin to search for the best solution from an empty solution and incrementally expand their solutions during the search process.…”

Section: Bco Main Principlesmentioning

confidence: 99%

“…As it is already mentioned, the searching procedure in BCO algorithm is performed iteratively 10,11 . Each iteration contains a number of steps (S) and each step consists of the so called forward pass (FP) and backward pass (BP), which are performed alternatively.…”

Section: Bco Main Principlesmentioning

confidence: 99%

“…[10][11][12][13] The number of solution modifications (M) within each forward pass (FP) could be specified in advance or randomly chosen at the beginning of each forward pass. After M modifications of solutions performed by all bees, the first forward pass is finished and the second phase of the BCO step, the so-called backward pass (BP) begins.…”

Section: Bco Main Principlesmentioning

confidence: 99%

“…In this paper, the bee colony optimization (BCO) metaheuristic is applied to solve the revenue maximization (or shortly Max-Rev) problem in optical WDM networks. Various studies proved that BCO is a fast, robust and efficient optimization tool, which can produce high quality solutions together with small computational complexity [7][8][9][10][11][12][13][14][15] . A comprehensive overview of BCO metaheuristic with its various applications could be found in Refs.…”

Section: Introductionmentioning

confidence: 99%

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Revenue-driven Lightpaths Provisioning over Optical WDM Networks Using Bee Colony Optimization

Marković¹

2017

IJCIS

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This paper aims to study the lightpaths provisioning problem in optical WDM networks with scarce available wavelengths under the static (off-line) traffic demands such that network operator's (NO's) revenue is maximized. To achieve this goal, a NO has to be addressed with the issue how to solve the call admission control jointly with the lightpaths routing and wavelength assignment (RWA) problem in efficient manner. The improved bee colony optimization (BCOi) metaheuristic is applied to solve the considered revenue maximization (Max-Rev) problem. We evaluated the performances of the proposed BCOi Max-Rev framework by performing numerous simulation experiments in different realistic WDM optical network topologies. We observed that our BCOi Max-Rev algorithm is an efficient tool to produce high quality solutions within reasonable amount of CPU time. It has been proved that BCOi Max-Rev solutions just slightly deviate from optimal solutions (at most 1%) and considerably outperform some heuristic algorithms, such as the Max-Profit and FCFS. In addition, our Max-Rev BCOi algorithm is able to produce better solution quality compared to the constructive BCO approach (up to 3.5% in the case of NSFNet and 5% in the case of EON). Finally, we compared the BCOi to differential evolution (DE) approach in the case of more complex networks, such as the USA optical network topology. The results show that our BCOi always outperforms DE metaheuristic, whereby the profit improvement could reach up to 20 % in some instances.

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Section: Bco Main Principlesmentioning

confidence: 99%

Section: Bco Main Principlesmentioning

confidence: 99%

Section: Bco Main Principlesmentioning

confidence: 99%

Section: Bco Main Principlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revenue-driven Lightpaths Provisioning over Optical WDM Networks Using Bee Colony Optimization

Marković¹

2017

IJCIS

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A survey on energy awareness mechanisms in routing protocols for wireless sensor networks using optimization methods

Saleh

Ali

Rasid

et al. 2013

Trans. Emerging Tel. Tech.

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Over recent years, enormous amounts of research in wireless sensor networks (WSNs) have been conducted; this is because of its multifarious applications such as in environmental monitoring, object tracking, disaster management, manufacturing, monitoring and control. Nevertheless, optimising the energy consumption of sensor nodes (SNs) in WSNs and realising the anticipated quality of solutions supplied from them present considerable challenges. In WSNs, the sudden ‘death’ of critical nodes can cause an entire network to malfunction. This is usually caused by uneven depletion of battery power of the individual nodes. Therefore, this survey investigates the methods that aim to minimise usage of energy in SNs, especially during routing. This is because network activities start to be challenged when the first SN exhausts its battery. In addition, this paper introduces a comprehensive categorization of the energy‐aware protocols in WSNs. Thus, the motivation of this survey is to present some decentralised energy‐aware routing using optimization methods that is generic and applicable to most of WSN applications that require reduction of energy consumptions and extend a network's life time. Copyright © 2013 John Wiley & Sons, Ltd.

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A metaheuristic approach for source traffic grooming in elastic optical networks

Miladić-Tešić

Marković

Nonković

et al. 2020

Trans Emerging Tel Tech

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The increasing demands for higher spectrum efficiency have made elastic optical networks (EON) to be an interesting area for the research community. The possibility that some network parameters are changed according to different types of services allows that optical paths can be established using just enough spectrum as needed. Since individual optical paths have to be separated by guard bands, a large part of the available spectrum is wasted. The efficient utilization of available network capacity is among network operators' major concerns and therefore, traffic engineering techniques that provide additional capacity on the existing infrastructure are of the interest. As such, the traffic grooming technique has been analyzed in this article. By applying the grooming technique, the basic problem in EONs, namely routing and spectrum allocation is extended and made more complex, especially in the case of realistic networks. Compared to electrical traffic grooming applied in traditional wavelength division multiplexing networks, traffic grooming directly at the optical layer is an attractive option for EON because it provides the opportunity to groom several same source connections into one transmitter and in such a way eliminating the guard bands. In this article, we solve the grooming problem in EON with static traffic demands using the bee colony optimization metaheuristic approach. The objective is to serve all traffic demands such that spectrum savings are maximized and total spectrum usage is minimized. The results indicate considerable spectrum savings compared to the scenario without grooming and a significant improvement considering some referenced grooming algorithms.

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Bee Colony Optimization - part II: The application survey

Cited by 23 publications

References 35 publications

Revenue-driven Lightpaths Provisioning over Optical WDM Networks Using Bee Colony Optimization

Revenue-driven Lightpaths Provisioning over Optical WDM Networks Using Bee Colony Optimization

A survey on energy awareness mechanisms in routing protocols for wireless sensor networks using optimization methods

A metaheuristic approach for source traffic grooming in elastic optical networks

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