Title | ||
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Solving The Non-Split Weighted Ring Arc-Loading Problem In A Resilient Packet Ring Using Particle Swarm Optimization |
Abstract | ||
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Massive growth of the Internet traffic in last decades has motivated the design of high-speed optical networks. Resilient Packet Ring (RPR), also known as IEEE 802.17, is a standard designed for the optimized transport of data traffic over optical fiber ring networks. Its design is to provide the resilience found in SONET/SDH networks but instead of setting up circuit oriented connections, providing a packet based transmission. This is to increase the efficiency of Ethernet and IF services. In this paper, a weighted ring arc-loading problem (WRALP) is considered which arises in engineering and planning of the RPR systems (combinatorial optimization NP- complete problem). Specifically, for a given set of non-split and uni-directional point-to-point demands (weights), the objective is to find the routing for each demand (i.e., assignment of the demand to either clockwise or counter-clockwise ring) so that the maximum arc load is minimized. This paper suggests four variants of Particle Swarm Optimization (PSO), combined with a Local Search (LS) method to efficient non-split traffic loading on the RPR. Numerical simulation results show the effectiveness and efficiency of the proposed methods. |
Year | Venue | Keywords |
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2009 | IJCCI 2009: PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE | Weighted ring Arc-Loading problem, Particle swarm optimization, Local search, Optimization |
Field | DocType | Citations |
Particle swarm optimization,Topology,Mathematical optimization,Arc (geometry),Computer science,Resilient Packet Ring,Multi-swarm optimization | Conference | 4 |
PageRank | References | Authors |
0.43 | 1 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Anabela Moreira Bernardino | 1 | 75 | 9.44 |
Eugénia Moreira Bernardino | 2 | 75 | 9.44 |
Juan Manuel Sánchez-Pérez | 3 | 219 | 28.40 |
Juan Antonio Gómez-Pulido | 4 | 334 | 43.02 |
Miguel Ángel Vega Rodríguez | 5 | 22 | 4.16 |