Szczegóły publikacji

Opis bibliograficzny

A multi-objective memetic inverse solver reinforced by local optimization methods / Ewa GAJDA-ZAGÓRSKA, Robert SCHAEFER, Maciej SMOŁKA, David Pardo, Julen Álvarez-Aramberri // Journal of Computational Science ; ISSN 1877-7503. — 2017 — vol. 18, s. 85–94. — Bibliogr. s. 92–93, Abstr. — Publikacja dostępna online od: 2016-07-28. — E. Gajda-Zagórska - pierwsza afiliacja: IST Austria

Autorzy (5)

Słowa kluczowe

multi-objective optimization methodsmemetic algorithmsinverse problems

Dane bibliometryczne

ID BaDAP104786
Data dodania do BaDAP2017-05-08
Tekst źródłowyURL
DOI10.1016/j.jocs.2016.06.007
Rok publikacji2017
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Czasopismo/seriaJournal of Computational Science

Abstract

We propose a new memetic strategy that can solve the multi-physics, complex inverse problems, formulated as the multi-objective optimization ones, in which objectives are misfits between the measured and simulated states of various governing processes. The multi-deme structure of the strategy allows for both, intensive, relatively cheap exploration with a moderate accuracy and more accurate search many regions of Pareto set in parallel. The special type of selection operator prefers the coherent alternative solutions, eliminating artifacts appearing in the particular processes. The additional accuracy increment is obtained by the parallel convex searches applied to the local scalarizations of the misfit vector. The strategy is dedicated for solving ill-conditioned problems, for which inverting the single physical process can lead to the ambiguous results. The skill of the selection in artifact elimination is shown on the benchmark problem, while the whole strategy was applied for identification of oil deposits, where the misfits are related to various frequencies of the magnetic and electric waves of the magnetotelluric measurements. (C) 2016 Elsevier B.V. All rights reserved.

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#95824Data dodania: 9.2.2016
Multi-objective hierarchic memetic solver for inverse parametric problems / Ewa GAJDA-ZAGÓRSKA, Maciej SMOŁKA, Robert SCHAEFER, David Pardo, Julen Álvarez-Aramberri // Procedia Computer Science [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1877-0509. — 2015 — vol. 51, s. 974–983. — Bibliogr. s. 982–983, Abstr. — ICCS 2015 : 15th annual International Conference on Computational Science : June 1–3, 2015, Reykjavík, Iceland
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#122014Data dodania: 28.6.2019
Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations / Jakub SAWICKI, Marcin ŁOŚ, Maciej SMOŁKA, Julen Alvarez-Aramberri // Journal of Computational Science ; ISSN 1877-7503. — 2019 — vol. 34, s. 48–54. — Bibliogr. s. 54, Abstr. — Publikacja dostępna online od: 2019-04-20