MILP models for objective reduction in multi-objective optimization: Error measurement considerations and non-redundancy ratio

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Title: MILP models for objective reduction in multi-objective optimization: Error measurement considerations and non-redundancy ratio
Authors: Vázquez, Daniel | Ruiz-Femenia, Rubén | Jiménez, Laureano | Caballero, José A.
Research Group/s: Computer Optimization of Chemical Engineering Processes and Technologies (CONCEPT)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Química | Universidad de Alicante. Instituto Universitario de Ingeniería de los Procesos Químicos
Keywords: MOO objective reduction | Non-Redundancy ratio | PCA | δ-error | Deb & Saxena algorithm
Knowledge Area: Ingeniería Química
Issue Date: 12-Jul-2018
Publisher: Elsevier
Citation: Computers & Chemical Engineering. 2018, 115: 323-332. doi:10.1016/j.compchemeng.2018.04.031
Abstract: A common approach in multi-objective optimization (MOO) consists of removing redundant objectives or reducing the set of objectives minimizing some metrics related with the loss of the dominance structure. In this paper, we comment some weakness related to the usual minimization of the maximum error (infinity norm or δ-error) and the convenience of using a norm 1 instead. Besides, a new model accounting for the minimum number of Pareto solutions that are lost when reducing objectives is provided, which helps to further describe the effects of the objective reduction in the system. A comparison of the performance of these algorithms and its usefulness in objective reduction against principal component analysis + Deb & Saxena's algorithm (Deb & Saxena Kumar, 2005) is provided, and the ability of combining it with a principal component analysis in order to reduce the dimensionality of a system is also studied and commented.
Sponsor: The authors acknowledge financial support from the Spanish “Ministerio de Economía, Industria y Competitividad” (CTQ2016-77968-C3-2-P, AEI/FEDER, UE).
ISSN: 0098-1354 (Print) | 1873-4375 (Online)
DOI: 10.1016/j.compchemeng.2018.04.031
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Elsevier Ltd.
Peer Review: si
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Appears in Collections:INV - CONCEPT - Artículos de Revistas

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