MILP method for objective reduction in multi-objective optimization

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Title: MILP method for objective reduction in multi-objective optimization
Authors: Vázquez, Daniel | Fernandez-Torres, Maria J. | 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: Multi-objective optimization | δ-MOSS | k-EMOSS | Objective reduction | Dominance structure | Pareto frontier
Knowledge Area: Ingeniería Química
Issue Date: 4-Jan-2018
Publisher: Elsevier
Citation: Computers & Chemical Engineering. 2018, 108: 382-394. doi:10.1016/j.compchemeng.2017.10.021
Abstract: A procedure for reducing objectives in a multi-objective optimization problem given a set of Pareto solutions is presented. Three different models are detailed, which achieve three different degrees of objective reduction. These models are based on maintaining the dominance structure of the problem. To compare the performance of the proposed models, these are tested with pure mathematical cases and with actual data from previous works in the field of multi-objective optimization. The first model provides the reduced subset of objectives that do not alter the dominance structure of the problem at all. The second model determines the minimum subset of objectives that alters the dominance structure with an upper predefined limit for the error. The last model provides the subset of objectives with a previously defined cardinality, which achieves the minimum error. The possibility of different inputs introduces flexibility into the models, which accounts for the preferences of the decision-maker.
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.2017.10.021
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 Elsevier Ltd.
Peer Review: si
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Appears in Collections:INV - CONCEPT - Artículos de Revistas

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