Tornado: An Autonomous Chaotic Algorithm for High Dimensional Global Optimization Problems

Aslimani, Nassime; Talbi, El-Ghazali; Ellaia, Rachid

Type de document :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-031-34020-8_1

Titre :

Tornado: An Autonomous Chaotic Algorithm for High Dimensional Global Optimization Problems

Auteur(s) :

Aslimani, Nassime [Auteur]
Ecole Mohammadia d'Ingénieurs [EMI]
Talbi, El-Ghazali [Auteur]

Optimisation de grande taille et calcul large échelle [BONUS]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Ellaia, Rachid [Auteur]
Ecole Mohammadia d'Ingénieurs [EMI]

Titre de la manifestation scientifique :

International Conference on Optimization and Learning

Ville :

Malaga

Pays :

Espagne

Date de début de la manifestation scientifique :

2023-05

Titre de la revue :

Communications in Computer and Information Science

Éditeur :

Springer Nature Switzerland

Lieu de publication :

Cham

Date de publication :

2023-05-27

Mot(s)-clé(s) en anglais :

Fractal optimization

Discipline(s) HAL :

Informatique [cs]

Résumé en anglais : [en]

In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for high dimensional global optimization problems. The algorithm introduces advanced symmetrization, levelling and fine search strategies ...
Lire la suite >In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for high dimensional 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 was used to assess the performance of the proposed chaotic algorithm. The obtained results have 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 metaheuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm.A panel of various benchmark problems with different properties was used to assess the performance of the proposed chaotic algorithm. The obtained results have 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 metaheuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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