A multi-objective memetic inverse solver reinforced by local optimization methods
Autor: | Maciej Smołka, Julen Álvarez-Aramberri, David Pardo, Robert Schaefer, Ewa Gajda-Zagórska |
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Rok vydání: | 2016 |
Předmět: |
Inverse problems
Mathematical optimization General Computer Science Pareto principle Inverse 02 engineering and technology Inverse problem Solver 010502 geochemistry & geophysics 01 natural sciences Theoretical Computer Science Set (abstract data type) Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Memetic algorithms Memetic algorithm Multi-objective optimization methods 020201 artificial intelligence & image processing Selection (genetic algorithm) 0105 earth and related environmental sciences Mathematics |
Zdroj: | BIRD: BCAM's Institutional Repository Data instname Journal of Computational Science |
Popis: | 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. |
Databáze: | OpenAIRE |
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