<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

An agent-oriented hierarchic strategy for solving inverse problems

Department of Computer Science AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland

Department of Applied Mathematics, Statistics, and Operational Research University of the Basque Country (UPV/EHU), Barrio Sarriena S/N, 48940 Leioa (Bizkaia), Spain

Basque Center for Applied Mathematics (BCAM), Alameda de Mazarredo 14, 48009 Bilbao, Spain

Ikerbasque (Basque Foundation for Sciences), Maria Diaz de Haro 3, 48013 Bilbao, Spain

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"An agent-oriented hierarchic strategy for solving inverse problems"}

The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond...

An agent-oriented hierarchic strategy for solving inverse problems

Article Category: Special Section Title: Agents in Intelligent Computing and Simulation Systems, Editors: Aleksander Byrski, Marek Kisiel-Dorohinicki, Grzegorz Dobrowolski

Published Online: Sep 30, 2015

Page range: 483 - 498

Received: Sep 05, 2014

DOI: https://doi.org/10.1515/amcs-2015-0036

© by Maciej Smołka

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.