Bee Colony Optimization - part I: The algorithm overview

Davidović, Tatjana; Teodorović, Dušan; Šelmić, Milica

doi:10.2298/yjor131011017d

Cited by 50 publications

(29 citation statements)

References 45 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%

“…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: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Marković¹

2017

IJCIS

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“…BCO digunakan sebagai solusi pencarian terbaik untuk masalah optimasi. Berdasarkan penelitian yang menjelaskan inspirasi untuk menciptakan sistem multi-agen seperti BCO, metode ini berasal dari perilaku mencari makan dari lebah, yaitu madu [18]. Perilaku ini cocok untuk pemodelan, karena praktik pengumpulan dan pengolahan nektar sangat teroganisir.…”

Section: Bee Colony Optimization (Bco)unclassified

Peningkatan Akurasi Estimasi Usaha dan Biaya COCOMO II Berdasarkan Gaussian dan BCO

Putri¹,

Siahaan²,

Sarwosri³

2017

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Abstract-An accurate effort and cost estimation provides good management for software projects. Less accurate estimation will affect the management of the software project and cause the ineffectiveness of the project development process. The addition of cost driver, introduced by Barry Boehm in 2000, is used in this paper to provide better accuracy, because it has covered the entire section in the estimation. However, in this paper, the accuracy of effort and cost estimation by COCOMO II Fuzzy Gaussian method is still far from actual effort. Therefore, the accuracy can still be increased using Bee Colony Optimization (BCO), as seen in the MMRE loyal results. The value of parameter A and B on COCOMO II is also changed with the initial gradual of 0.01 to give optimal value on a certain gradual. Based on the result of the implementation, the error accuracy of effort estimation and software project cost is reduced by 38%, compared to previous research. In conclusion, the proposed method can increase the accuracy of effort and cost estimation.Intisari-Perkiraan usaha dan biaya yang akurat memberikan manajemen yang baik untuk proyek perangkat lunak. Perkiraan usaha dan biaya yang kurang akurat akan memengaruhi manajemen proyek perangkat lunak dan menyebabkan kurang efektifnya proses pengembangan proyek tersebut. Penambahan cost driver yang diperkenalkan Barry Boehm pada tahun 2000 digunakan dalam makalah ini guna memberikan akurasi yang lebih baik, karena telah mencakup keseluruhan bagian yang diestimasi. Namun, dalam makalah ini akurasi hasil perkiraan usaha dan biaya dengan metode COCOMO II Fuzzy Gaussian masih jauh dari Actual Effort. Oleh sebab itu, peningkatan akurasi hasil COCOMO II Fuzzy Gaussian masih dapat dilakukan dengan menggunakan metode Bee Colony Optimization (BCO) yang meliputi hasil optimasi yang lebih baik, terlihat dari hasil MMRE loyal. Tidak hanya menggunakan metode BCO saja, tetapi juga dilakukan perubahan nilai parameter A dan B pada COCOMO II dengan gradual awal adalah 0,01 untuk mencapai nilai optimal pada gradual tertentu. Berdasarkan hasil implementasi metode yang diusulkan, kesalahan akurasi perkiraan usaha dan biaya proyek perangkat lunak dapat turun 38%, dibandingkan dengan penelitian sebelumnya. Jadi, metode yang digunakan berhasil meningkatkan akurasi perkiraan usaha dan biaya.Kata Kunci-Usaha, perkiraan biaya proyek perangkat lunak, Constructive Cost Model II, akurasi, Fuzzy Gaussian, Bee Colony Optimization.I. PENDAHULUAN Pengembangan perangkat lunak merupakan proses yang tidak sepenuhnya sempurna. Di dalamnya sering terdapat keterlambatan, kegagalan, hingga ditolaknya suatu proyek perangkat lunak. Sebagai contoh perkiraan, manajemen, efisiensi biaya, dan usaha seluruh proyek, hal tersebut adalah faktor-faktor penting dalam pengukuran perkiraan proyek perangkat lunak. Manajemen yang baik dari pengembangan perangkat lunak dapat memperkirakan biaya dan sumber daya perangkat lunak yang tepat. Perkiraan biaya proyek adalah tugas memprediksi biaya, usaha, atau produktifitas yang dibutuhkan...

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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 I: The algorithm overview

Cited by 50 publications

References 45 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

Peningkatan Akurasi Estimasi Usaha dan Biaya COCOMO II Berdasarkan Gaussian dan BCO

A metaheuristic approach for source traffic grooming in elastic optical networks

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