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https://www.um.edu.mt/library/oar/handle/123456789/98668Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Farrugia, Noel | - |
| dc.contributor.author | Briffa, Johann A. | - |
| dc.contributor.author | Buttigieg, Victor | - |
| dc.date.accessioned | 2022-07-01T08:58:46Z | - |
| dc.date.available | 2022-07-01T08:58:46Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Farrugia, N., Briffa, J. A., & Buttigieg, V. (2019, June). Solving the multi-commodity flow problem using a multi-objective genetic algorithm. In 2019 IEEE Congress on Evolutionary Computation (CEC). 2816-2823. | en_GB |
| dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/98668 | - |
| dc.description.abstract | A Multi-Objective Genetic Algorithm (MOGA) designed to solve the Multi-Commodity Flow Problem (MCFP) with the aim of improving network efficiency is presented. This work improves on our previous MOGA, using new objectives that better represent the routing solutions we seek. The new algorithm increases the total network flow by 6% and 25% when compared with a set up similar to OSPF and our previous work, respectively, without resorting to multipath routing. Network simulations for TCP flows show that our proposed algorithm achieves the highest total network flow and the lowest number of unallocated flows when compared with our previous MOGA, the OSPF-like setup, and the optimal path-constrained Maximum-Flow MinimumCost solution. The flow delay performance is similar to the other algorithms, even though the proposed algorithm is pushing more data onto the network. | en_GB |
| dc.language.iso | en | en_GB |
| dc.publisher | IEEE | en_GB |
| dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
| dc.subject | Genetic algorithms | en_GB |
| dc.subject | Computer networks | en_GB |
| dc.subject | Linear programming | en_GB |
| dc.title | Solving the multi-commodity flow problem using a multi-objective genetic algorithm | en_GB |
| dc.type | conferenceObject | en_GB |
| dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
| dc.bibliographicCitation.conferencename | 2019 IEEE Congress on Evolutionary Computation (CEC) | en_GB |
| dc.bibliographicCitation.conferenceplace | Wellington, New Zealand, 13/06/2019 | en_GB |
| dc.description.reviewed | peer-reviewed | en_GB |
| dc.identifier.doi | 10.1109/CEC.2019.8790160 | - |
| Appears in Collections: | Scholarly Works - FacICTCCE | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Solving_the_Multi-Commodity_Flow_Problem_using_a_Multi-Objective_Genetic_Algorithm_2019.pdf Restricted Access | 537.36 kB | Adobe PDF | View/Open Request a copy |
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