---
_id: '1152'
abstract:
- lang: eng
  text: 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. 2016 Elsevier B.V.
article_processing_charge: No
author:
- first_name: Ewa P
  full_name: Gajda-Zagorska, Ewa P
  id: 47794CF0-F248-11E8-B48F-1D18A9856A87
  last_name: Gajda-Zagorska
- first_name: Robert
  full_name: Schaefer, Robert
  last_name: Schaefer
- first_name: Maciej
  full_name: Smołka, Maciej
  last_name: Smołka
- first_name: David
  full_name: Pardo, David
  last_name: Pardo
- first_name: Julen
  full_name: Alvarez Aramberri, Julen
  last_name: Alvarez Aramberri
citation:
  ama: Gajda-Zagorska EP, Schaefer R, Smołka M, Pardo D, Alvarez Aramberri J. A multi
    objective memetic inverse solver reinforced by local optimization methods. <i>Journal
    of Computational Science</i>. 2017;18:85-94. doi:<a href="https://doi.org/10.1016/j.jocs.2016.06.007">10.1016/j.jocs.2016.06.007</a>
  apa: Gajda-Zagorska, E. P., Schaefer, R., Smołka, M., Pardo, D., &#38; Alvarez Aramberri,
    J. (2017). A multi objective memetic inverse solver reinforced by local optimization
    methods. <i>Journal of Computational Science</i>. Elsevier. <a href="https://doi.org/10.1016/j.jocs.2016.06.007">https://doi.org/10.1016/j.jocs.2016.06.007</a>
  chicago: Gajda-Zagorska, Ewa P, Robert Schaefer, Maciej Smołka, David Pardo, and
    Julen Alvarez Aramberri. “A Multi Objective Memetic Inverse Solver Reinforced
    by Local Optimization Methods.” <i>Journal of Computational Science</i>. Elsevier,
    2017. <a href="https://doi.org/10.1016/j.jocs.2016.06.007">https://doi.org/10.1016/j.jocs.2016.06.007</a>.
  ieee: E. P. Gajda-Zagorska, R. Schaefer, M. Smołka, D. Pardo, and J. Alvarez Aramberri,
    “A multi objective memetic inverse solver reinforced by local optimization methods,”
    <i>Journal of Computational Science</i>, vol. 18. Elsevier, pp. 85–94, 2017.
  ista: Gajda-Zagorska EP, Schaefer R, Smołka M, Pardo D, Alvarez Aramberri J. 2017.
    A multi objective memetic inverse solver reinforced by local optimization methods.
    Journal of Computational Science. 18, 85–94.
  mla: Gajda-Zagorska, Ewa P., et al. “A Multi Objective Memetic Inverse Solver Reinforced
    by Local Optimization Methods.” <i>Journal of Computational Science</i>, vol.
    18, Elsevier, 2017, pp. 85–94, doi:<a href="https://doi.org/10.1016/j.jocs.2016.06.007">10.1016/j.jocs.2016.06.007</a>.
  short: E.P. Gajda-Zagorska, R. Schaefer, M. Smołka, D. Pardo, J. Alvarez Aramberri,
    Journal of Computational Science 18 (2017) 85–94.
date_created: 2018-12-11T11:50:26Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2023-09-20T11:29:44Z
day: '01'
ddc:
- '000'
department:
- _id: ChWo
doi: 10.1016/j.jocs.2016.06.007
external_id:
  isi:
  - '000393528700009'
file:
- access_level: open_access
  content_type: application/pdf
  creator: dernst
  date_created: 2019-01-18T08:43:16Z
  date_updated: 2019-01-18T08:43:16Z
  file_id: '5842'
  file_name: 2016_jocs_ewa.pdf
  file_size: 1083911
  relation: main_file
  success: 1
file_date_updated: 2019-01-18T08:43:16Z
has_accepted_license: '1'
intvolume: '        18'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Submitted Version
page: 85 - 94
publication: Journal of Computational Science
publication_identifier:
  issn:
  - '18777503'
publication_status: published
publisher: Elsevier
publist_id: '6206'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A multi objective memetic inverse solver reinforced by local optimization methods
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 18
year: '2017'
...