CIIC - Publicações em Atas de Conferências com Peer Review
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- Hybrid Honey Bees Mating Optimisation algorithm to assign terminals to concentratorsPublication . Bernardino, Eugénia M.; Bernardino, Anabela M.; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelIn this paper we propose a new approach to assign terminals to concentrators using a Hybrid Honey Bees Mating Optimisation algorithm. Honey Bees Mating Optimisation (HBMO) algorithm is a swarm-based optimisation algorithm, which simulates the mating process of real honey bees. We apply a hybridisation of HBMO to solve a combinatorial optimisation problem known as Terminal Assignment Problem (TAP). The purpose is to connect a given set of terminals to a given set of concentrators and minimise the link cost to form a communication network. The feasibility of Hybrid HBMO is demonstrated and compared with the solutions obtained by other algorithms from literature over different TAP instances.
- A Hybrid Population-Based Incremental Learning algorithm for load balancing in RPRPublication . Bernardino, Anabela M.; Bernardino, Eugénia M.; Sánchez-Pérez, Juan Manuel; Gómez-Pulido, Juan Antonio; Vega-Rodríguez, Miguel AngelWhen managed properly, the ring networks are uniquely suited to deliver a large amount of bandwidth in a reliable and inexpensive way. An optimal load balancing is very important, because it increases the system capacity and improves the overall ring performance. An important optimisation problem in this context is the Weighted Ring Arc Loading Problem (WRALP). It consists of the design, in a communication network of a transmission route (direct path) for each request, such that high load on the ring arcs will be avoided. WRALP asks for a routing scheme such that the maximum load on the ring arcs will be minimum. In this paper we study WRALP without demand splitting and we propose a Hybrid Populationbased Incremental Learning (HPBIL) to solve it. We show that HPBIL is able to achieve good solutions, improving the results obtained by previous approaches.