Depot Institutionnel de l'UMBB >
Publications Scientifiques >
Communications Nationales >
Veuillez utiliser cette adresse pour citer ce document :
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7660
|
Titre: | Three computational intelligence methods for system reliability |
Auteur(s): | Chebouba, Billal Nazim Mellal, Mohamed Arezki Adjerid, Smail |
Mots-clés: | Particle Swarm Optimization Reliability-Redundancy allocation, Stochastic Fractal Search, Cuckoo Search, Particle Swarm Optimization Reliability-Redundancy allocation |
Date de publication: | 2018 |
Collection/Numéro: | Conference: The Second Workshop on Signal Processing Applied to Rotating Machinery DiagnosticsAt: Djelfa, Algeria; |
Résumé: | Nowadays, the competitiveness in the industrial world became more and
more harsh, which requires that the system must be as reliable as possible. In many
optimization problems, hard fitness functions are considered. Those functions can-
not be solved by the traditional mathematical programming methods. An alternative
solution to the conventional approaches. The meta-heuristic optimization tech-
niques are used, due to their ability to obtain global or near-global optimum solu-
tions. In the present paper, we address the system reliability-redundancy allocation
optimization problem, using three well known algorithms namely, the Cuckoo
Search (CS), Particle Swarm Optimization (PSO), and the Fractal Search (SFS).
The constraints defined here in the problem are handled with the help of the penalty
function method. The results of the numerical case study are compared for high-
lighting the superiority of an algorithm over another. |
URI/URL: | http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7660 |
Collection(s) : | Communications Nationales
|
Fichier(s) constituant ce document :
|
Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.
|