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Título: QUANTUM-INSPIRED GENETIC ALGORITHMS APPLIED TO ORDERING COMBINATORIAL OPTIMIZATION PROBLEMS
Instituição: ---
Autor(es): LUCIANO REIS DA SILVEIRA
RICARDO TANSCHEIT
MARLEY MARIA BERNARDES REBUZZI VELLASCO
Colaborador(es): ---
Catalogação: 14/02/2013
Tipo: PAPER Idioma(s): ENGLISH - UNITED STATES
Nota:
© 2012 IEEE. Reprinted, with permission, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, JUNE 2012. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Pontifícia Universidade Catolica do Rio de Janeiro’s. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyrightlaws protecting it.
Referência [en]: https://www.maxwell.vrac.puc-rio.br/eletricaonline/serieConsulta.php?strSecao=resultado&nrSeq=21153@2
Resumo:
This article proposes a new algorithm based on evolutionary computation and quantum computing. It attempts to resolve ordering combinatorial optimization problems, the most well known of which is the traveling salesman problem (TSP). Classic and quantum-inspired genetic algorithms based on binary representations have been previously used to solve combinatorial optimization problems. However, for ordering combinatorial optimization problems, order-based genetic algorithms are more adequate than those with binary representation, since a specialized crossover process can be employed in order to always generate feasible solutions. Traditional order-based genetic algorithms have already been applied to ordering combinatorial optimization problems but few quantum-inspired genetic algorithms have been proposed. The algorithm presented in this paper contributes to the quantum-inspired genetic approach to solve ordering combinatorial optimization problems. The performance of the proposed algorithm is compared with one orderbased genetic algorithm using uniform crossover. In all cases considered, the results obtained by applying the proposed algorithm to the TSP were better, both in terms of processing times and in terms of the quality of the solutions obtained, than those obtained with order-based genetic algorithms.
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