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Tornado: An Autonomous Chaotic Algorithm for Large Scale Global Optimization

Nassime Aslimani 1 El-Ghazali Talbi 1 Rachid Ellaia 2 
1 BONUS - Optimisation de grande taille et calcul large échelle
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for large scale global optimization problems. The algorithm introduces advanced symmetrization, levelling and fine search strategies for an efficient and effective exploration of the search space and exploitation of the best found solutions. To our knowledge, this is the first accurate and fast autonomous chaotic algorithm solving large scale optimization problems. A panel of various benchmark problems with different properties were used to assess the performance of the proposed chaotic algorithm. The obtained results has shown the scalability of the algorithm in contrast to chaotic optimization algorithms encountered in the literature. Moreover, in comparison with some state-of-the-art meta-heuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm.
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Submitted on : Monday, March 30, 2020 - 11:09:00 AM
Last modification on : Thursday, March 24, 2022 - 3:42:56 AM


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  • HAL Id : hal-02499326, version 2


Nassime Aslimani, El-Ghazali Talbi, Rachid Ellaia. Tornado: An Autonomous Chaotic Algorithm for Large Scale Global Optimization. 2020. ⟨hal-02499326v2⟩



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