Name
Abstract | ||
|---|---|---|
We propose a multi-objective approach for solving challenging inverse parametric problems. The objectives are misfits for several physical descriptions of a phenomenon under consideration, whereas their domain is a common set of admissible parameters. The resulting Pareto set, or parameters close to it, constitute various alternatives of minimizing individual misfits. A special type of selection applied to the memetic solution of the multi-objective problem narrows the set of alternatives to the ones that are sufficiently coherent. The proposed strategy is exemplified by solving a real-world engineering problem consisting of the magnetotelluric measurement inversion that leads to identification of oil deposits located about 3 km under the Earth's surface, where two misfit functions are related to distinct frequencies of the electric and magnetic waves. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.procs.2015.05.239 | INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE |
Keywords | Field | DocType |
inverse problems, multi-objective optimization methods, memetic algorithms | Memetic algorithm,Inverse,Mathematical optimization,Computer science,Inversion (meteorology),Parametric statistics,Artificial intelligence,Magnetotellurics,Inverse problem,Solver,Machine learning,Pareto principle | Conference |
Volume | ISSN | Citations |
51 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ewa Gajda | 1 | 29 | 4.03 |
| Maciej Smołka | 2 | 107 | 13.60 |
| Robert Schaefer | 3 | 101 | 10.99 |
| David Pardo | 4 | 196 | 21.19 |
| Julen Álvarez-Aramberri | 5 | 4 | 1.12 |