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Paper details
Number 3 - September 2015
Volume 25 - 2015
An agent-oriented hierarchic strategy for solving inverse problems
Maciej Smołka, Robert Schaefer, Maciej Paszyński, David Pardo, Julen Álvarez-Aramberri
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. 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.
Keywords
inverse problems, hybrid optimization methods, memetic algorithms, multi-agent systems, magnetotelluric data inversion