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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An extension of the non-inferior set estimation algorithm for many objectives

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Author(s):
Raimundo, Marcos M. [1] ; Ferreira, V, Paulo A. ; Von Zuben, Fernando J. [2]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Sch Elect & Comp Engn, UNICAMP, Av Albert Einstein 400, BR-13083852 Campinas, SP - Brazil
[2] Ferreira, Paulo A., V, Univ Estadual Campinas, Sch Elect & Comp Engn, UNICAMP, Av Albert Einstein 400, BR-13083852 Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: European Journal of Operational Research; v. 284, n. 1, p. 53-66, JUL 1 2020.
Web of Science Citations: 0
Abstract

This work proposes a novel multi-objective optimization approach that globally finds a representative non-inferior set of solutions, also known as Pareto-optimal solutions, by automatically formulating and solving a sequence of weighted sum method scalarization problems. The approach is called MONISE (Many-Objective NISE) because it represents an extension of the well-known non-inferior set estimation (NISE) algorithm, which was originally conceived to deal with two-dimensional objective spaces. The proposal is endowed with the following characteristics: (1) uses a mixed-integer linear programming formulation to operate in two or more dimensions, thus properly supporting many (i.e., three or more) objectives; (2) relies on an external algorithm to solve the weighted sum method scalarization problem to optimality; and (3) creates a faithful representation of the Pareto frontier in the case of convex problems, and a useful approximation of it in the non-convex case. Moreover, when dealing specifically with two objectives, some additional properties are portrayed for the estimated non-inferior set. Experimental results validate the proposal and indicate that MONISE is competitive, in convex and non-convex (combinatorial) problems, both in terms of computational cost and the overall quality of the non-inferior set, measured by the acquired hypervolume. (C) 2019 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/13533-0 - Multi-objective optimization in multi-task learning
Grantee:Marcos Medeiros Raimundo
Support Opportunities: Scholarships in Brazil - Doctorate