Conception de métaheuristiques pour l'optimisation dynamique : application à l'analyse de séquences d'images IRM

Abstract : Many real-world problems are dynamic, i.e. their objective function (or cost function) changes over time. The main approach used in the literature is to adapt static optimization algorithms to dynamic optimization, compensating for their intrinsic defects. Rather than adopting this approach, already widely investigated, the main goal of this thesis is to develop an algorithm completely designed for dynamic optimization. The first part of this thesis is then devoted to the design of an algorithm, that should not only stand out from competing algorithms for its originality, but also perform better. In this context, our goal is to develop a dynamic optimization metaheuristic. Two agent-based algorithms, MADO (MultiAgent algorithm for Dynamic Optimization) and MLSDO (Multiple Local Search algorithm for Dynamic Optimization), are proposed and validated using the two main benchmarks available in dynamic environments : MPB (Moving Peaks Benchmark) and GDBG (Generalized Dynamic Benchmark Generator). The benchmark results obtained show the efficiency of the proposed algorithms, particularly : MLSDO is ranked at the first place among seven algorithms tested using GDBG, and at the second place among sixteen algorithms tested using MPB. Then, these algorithms are applied to real-world problems in medical image sequence processing (segmentation and registration of brain cine-MRI sequences). To our knowledge, this work is innovative in that the dynamic optimization approach had never been investigated for these problems. The performance gains obtained show the relevance of using the proposed dynamic optimization algorithms for this kind of applications
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Autre [cs.OH]. Université Paris-Est, 2011. Français. <NNT : 2011PEST1031>
Contributor : Abes Star <>
Submitted on : Tuesday, February 28, 2012 - 10:27:29 AM
Last modification on : Tuesday, August 26, 2014 - 4:06:55 PM
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  • HAL Id : tel-00674754, version 1



Julien Lepagnot. Conception de métaheuristiques pour l'optimisation dynamique : application à l'analyse de séquences d'images IRM. Autre [cs.OH]. Université Paris-Est, 2011. Français. <NNT : 2011PEST1031>. <tel-00674754>




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