@misc{Smołka_Maciej_An,
 author={Smołka, Maciej and Schaefer, Robert and Paszyński, Maciej and Pardo, David and Álvarez-Aramberri, Julen},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step.},
 abstract={The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems` difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified.},
 type={artykuł},
 title={An agent-oriented hierarchic strategy for solving inverse problems},
 keywords={inverse problems, hybrid optimization methods, memetic algorithms, multi-agent systems, magnetotelluric data inversion},
}