Deterministic metaheuristic based on Fractal Decomposition for large-scale Optimization

A. Nakib 1, 2 S. Ouchraa 2 N. Shvai 2 L. Souquet 3 E.-G. Talbi 4
1 SIMO
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
4 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
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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https://hal.archives-ouvertes.fr/hal-01681966
Contributeur : Lab Lissi <>
Soumis le : jeudi 11 janvier 2018 - 22:38:27
Dernière modification le : mardi 16 janvier 2018 - 01:13:04

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A. Nakib, S. Ouchraa, N. Shvai, L. Souquet, E.-G. Talbi. 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-01681966〉

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