Browsing by Author "Bernardino, Eugénia M."
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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.
- Solving the Ring Loading Problem Using Genetic Algorithms with Intelligent Multiple OperatorsPublication . Bernardino, Anabela M.; Bernardino, Eugénia M.; Sánchez-Pérez, Juan M.; Gómez-Pulido, Juan A.; Vega-Rodríguez, Miguel A.; Moreira Bernardino, Anabela; Bernardino, EugéniaPlanning optical communication networks suggests a number of new optimization problems, most of them in the field of combinatorial optimization. We address here the Ring Loading Problem. The objective of the problem is to find a routing scheme such that the maximum weighted load on the ring is minimized. In this paper we consider two variants: (i) demands can be split into two parts, and then each part is sent in a different direction; (ii) each demand must be entirely routed in either of the two directions, clockwise or counterclockwise. In this paper, we propose a genetic algorithm employing multiple crossover and mutation operators. Two sets of available crossover and mutation operators are established initially. In each generation a crossover method is selected for recombination and a mutation method is selected for mutation based on the amount fitness improvements achieve over a number of previous operations (recombinations/mutations). We use tournament selection for this purpose. Simulation results with the different methods implemented are compared.
- Solving the Terminal Assignment Problem Using a Local Search Genetic AlgorithmPublication . Bernardino, Eugénia M.; Bernardino, Anabela M.; Sánchez-Pérez, Juan M.; Gómez-Pulido, Juan A.; Vega-Rodríguez, Miguel A.; Bernardino, Eugénia; Moreira Bernardino, AnabelaTerminal assignment is an important issue in telecommunication networks optimization. The task here is to assign a given collection of terminals to a given collection of concentrators. The main objective is to minimize the link cost to form a network. This optimization task is an NP-complete problem. The intractability of this problem is a motivation for the pursuits of a local search genetic algorithm that produces approximate, rather than exact, solutions. In this paper, we explore one of the most successful emerging ideas combining local search with population-based search. Simulation results verify the effectiveness of the proposed method. The results show that our algorithm provides good solutions in a better running time.