Browsing by Author "Bernardino, Anabela Moreira"
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- Hybrid population-based incremental learning to assign terminals to concentratorsPublication . Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelIn the last decade, we have seen a significant growth in communication networks. In centralised communication networks, a central computer serves several terminals or workstations. In large networks, some concentrators are used to increase the network efficiency. A collection of terminals is connected to a concentrator and each concentrator is connected to the central computer. In this paper we propose a Hybrid Population-based Incremental Learning (HPBIL) to assign terminals to concentrators. We use this algorithm to determine the minimum cost to form a network by connecting a given collection of terminals to a given collection of concentrators. We show that HPBIL is able to achieve good solutions, improving the results obtained by previous approaches.
- Solving the ring arc-loading problem using a hybrid scatter search algorithmPublication . Bernardino, Anabela Moreira; Bernardino, Eugénia Moreira; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelResilient Packet Ring (RPR) is a standard that uses Ethernet switching and a dual counter-rotating ring topology to provide SONET-like network resiliency and optimised bandwidth usage, while it delivers multipoint Ethernet/IP services. An important optimisation problem arising in this context is the Weighted Ring Arc Loading Problem (WRALP). That is the design of a direct path for each request in a communication network, in such a way that high load on the arcs will be avoided, where an arc is an edge endowed with a direction. The load of an arc is defined as the total weight of those requests routed through the arc in its direction. WRALP ask for a routing scheme such that the maximum load on the arcs will be minimum. In this paper we study the loading problem without demand splitting and for solving it we propose a Hybrid Scatter Search (HSS) algorithm. Coupled with the Scatter Search algorithm we use a Tabu Search algorithm to locate the global minimum. We show that HSS is able to achieve feasible solutions to WRALP instances, improving the results obtained by previous approaches.
