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dc.contributor.authorSmolka, M.
dc.contributor.authorSchaefer, R.
dc.contributor.authorPaszynski, M.
dc.contributor.authorPardo, D. 
dc.contributor.authorAlvarez-Aramberri, J.
dc.description.abstractThe 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. 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.
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.subjecthybrid optimization methods
dc.subjectInverse problems
dc.subjectmagnetotelluric data inversion
dc.subjectmemetic algorithms
dc.subjectmulti-agent systems
dc.titleAn Agent-Oriented Hierarchic Strategy for Solving Inverse Problemsen_US
dc.journal.titleInternational Journal of Applied Mathematics and Computer Scienceen_US

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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España