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

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

Document type :

Communication dans un congrès avec actes

DOI :

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

Title :

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

Author(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]

Conference title :

International Conference on Optimization and Learning

City :

Malaga

Country :

Espagne

Start date of the conference :

2023-05

Journal title :

Communications in Computer and Information Science

Publisher :

Springer Nature Switzerland

Publication place :

Cham

Publication date :

2023-05-27

English keyword(s) :

Fractal optimization

HAL domain(s) :

Informatique [cs]

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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