Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/jspui/handle/123456789/7018
Title: Feedback-control operators for improved Pareto-set description : application to a polymer extrusion process.
Authors: Carrano, Eduardo Gontijo
Coelho, Dayanne Gouveia
Cunha, António Gaspar
Wanner, Elizabeth Fialho
Takahashi, Ricardo Hiroshi Caldeira
Keywords: Evolutionary computation
Multiobjective optimization
Genetic algorithms
Polymer extrusion
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Issue Date: 2015
Citation: CARRANO, E. G. et al. Feedback-control operators for improved Pareto-set description: application to a polymer extrusion process. Engineering Applications of Artificial Intelligence, v. 38, p. 147-167, 2015. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0952197614002565>. Acesso em: 07 ago. 2016.
Abstract: This paper presents a new class of operators for multiobjective evolutionary algorithms that are inspired on feedback-control techniques. The proposed operators, the archive-set reduction and the surface-filling crossover, have the purpose of enhancing the quality of the description of the Pareto-set in multiobjective optimization problems. They act on the Pareto -estimate sample set, performing operations that eliminate archive points in the most crowded regions, and generate new points in the less populated regions, leading to a dynamic equilibrium that tends to generate a uniform sampling of the efficient solution set. The internal parameters of those operators are coordinated by feedback-control inspired techniques, which ensure that the desired equilibrium is attained. Numerical experiments in some benchmark problems and in a real problem of optimization of a single screw extrusion system for polymer processing show that the proposed methodology is able to generate more detailed descriptions of Pareto-optimal fronts than the ones produced by usual algorithms.
URI: http://www.repositorio.ufop.br/handle/123456789/7018
metadata.dc.identifier.doi: https://doi.org/10.1016/j.engappai.2014.10.016
ISSN: 0952-1976
metadata.dc.rights.license: O periódico Engineering Applications of Artificial Intelligence concede permissão para depósito deste artigo no Repositório Institucional da UFOP. Número da licença: 3934300636088.
Appears in Collections:DECOM - Artigos publicados em periódicos

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