Publicação

Solving the Ring Loading Problem Using Genetic Algorithms with Intelligent Multiple Operators

Detalhes bibliográficos
Resumo:	Planning 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.
Autores principais:	Bernardino, Anabela M.
Outros Autores:	Bernardino, Eugénia M.; Sánchez-Pérez, Juan M.; Gómez-Pulido, Juan A.; Vega-Rodríguez, Miguel A.; Moreira Bernardino, Anabela; Bernardino, Eugénia
Assunto:	Optimization Genetic Algorithms Ring Loading Problem
Ano:	2009
País:	Portugal
Tipo de documento:	comunicação em conferência
Tipo de acesso:	acesso restrito
Instituição associada:	Instituto Politécnico de Leiria
Idioma:	inglês
Origem:	IC-online

Descrição
Resumo:	Planning 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.