Deterministic metaheuristic based on fractal decomposition for large-scale optimization

Amir Nakib 1 S. Ouchra 1 S. Chvai 1 El-Ghazali Talbi 2, 3 Léo Souquet 4
1 LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
3 Inria - Institut National de Recherche en Informatique et en Automatique
4 DSTI Labs - Data ScienceTech Institute
Abstract : In this work a new method based on geometric fractal decomposition to solve large-scale continuous optimization problems is proposed. It consists of dividing the feasible search space into sub-regions with the same geometrical pattern. At each iteration, the most promising ones are selected and further decomposed. This approach tends to provide a dense set of samples and has interesting theoretical convergence properties. Under some assumptions, this approach covers all the search space only in case of small dimensionality problems. The aim of this work is to propose a new algorithm based on this approach with low complexity and which performs well in case of large-scale problems. To do so, a low complex method that profits from fractals properties is proposed. Then, a deterministic optimization procedure is proposed using a single solution-based metaheuristic which is exposed to illustrate the performance of this strategy. Obtained results on common test functions were compared to those of algorithms from the literature and proved the efficiency of the proposed algorithm.
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Article dans une revue
Applied Soft Computing, Elsevier, 2017, 61, pp.468-485. 〈10.1016/j.asoc.2017.07.042〉
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https://hal.inria.fr/hal-01660190
Contributeur : Talbi El-Ghazali <>
Soumis le : dimanche 10 décembre 2017 - 11:15:39
Dernière modification le : vendredi 13 avril 2018 - 01:28:17

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INRIA | INRIA2017 | UPEC-UPEM | LISSI

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Amir Nakib, S. Ouchra, S. Chvai, El-Ghazali Talbi, Léo Souquet. Deterministic metaheuristic based on fractal decomposition for large-scale optimization. Applied Soft Computing, Elsevier, 2017, 61, pp.468-485. 〈10.1016/j.asoc.2017.07.042〉. 〈hal-01660190〉

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