A memetic procedure for global multi-objective optimization

  • In this paper we consider multi-objective optimization problems over a box. Several computational approaches to solve these problems have been proposed in the literature, that broadly fall into two main classes: evolutionary methods, which are usually very good at exploring the feasible region and retrieving good solutions even in the nonconvex case, and descent methods, which excel in efficiently approximating good quality solutions. In this paper, first we confirm, through numerical experiments, the advantages and disadvantages of these approaches. Then we propose a new method which combines the good features of both. The resulting algorithm, which we call Non-dominated Sorting Memetic Algorithm, besides enjoying interesting theoretical properties, excels in all of the numerical tests we performed on several, widely employed, test functions.

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Metadaten
Author:Matteo LapucciORCiD, Pierluigi MansuetoORCiD, Fabio SchoenORCiD
DOI:https://doi.org/10.1007/s12532-022-00231-3
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2022
Volume:15
Issue:2
Page Number:41
First Page:227
Last Page:267
Mathematical Programming Computation :MPC 2023 - Issue 2
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