Browsing by Author "Bernardino, Eugénia M."
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- 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.