{"year":"2017","publist_id":"6206","volume":18,"intvolume":"        18","ddc":["000"],"isi":1,"file_date_updated":"2019-01-18T08:43:16Z","publication_identifier":{"issn":["18777503"]},"external_id":{"isi":["000393528700009"]},"author":[{"id":"47794CF0-F248-11E8-B48F-1D18A9856A87","full_name":"Gajda-Zagorska, Ewa P","last_name":"Gajda-Zagorska","first_name":"Ewa P"},{"full_name":"Schaefer, Robert","last_name":"Schaefer","first_name":"Robert"},{"first_name":"Maciej","last_name":"Smołka","full_name":"Smołka, Maciej"},{"full_name":"Pardo, David","first_name":"David","last_name":"Pardo"},{"full_name":"Alvarez Aramberri, Julen","first_name":"Julen","last_name":"Alvarez Aramberri"}],"has_accepted_license":"1","publication":"Journal of Computational Science","doi":"10.1016/j.jocs.2016.06.007","page":"85 - 94","_id":"1152","language":[{"iso":"eng"}],"citation":{"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.","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.","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>","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.","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>."},"scopus_import":"1","publisher":"Elsevier","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","date_created":"2018-12-11T11:50:26Z","oa_version":"Submitted Version","oa":1,"status":"public","quality_controlled":"1","title":"A multi objective memetic inverse solver reinforced by local optimization methods","date_updated":"2023-09-20T11:29:44Z","month":"01","article_processing_charge":"No","day":"01","type":"journal_article","file":[{"date_updated":"2019-01-18T08:43:16Z","date_created":"2019-01-18T08:43:16Z","file_size":1083911,"success":1,"file_id":"5842","relation":"main_file","creator":"dernst","access_level":"open_access","file_name":"2016_jocs_ewa.pdf","content_type":"application/pdf"}],"abstract":[{"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.","lang":"eng"}],"department":[{"_id":"ChWo"}],"publication_status":"published","date_published":"2017-01-01T00:00:00Z"}