Authors:
Nadia Smairi
1
;
Sadok Bouamama
1
;
Khaled Ghedira
2
and
Patrick Siarry
3
Affiliations:
1
University of Manouba, Tunisia
;
2
University of Tunis, Tunisia
;
3
University of Paris, France
Keyword(s):
Particle Swarm Optimization, Tribes, Tabu Search, Multi-objective Optimization.
Related
Ontology
Subjects/Areas/Topics:
Evolutionary Computation and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
Abstract:
The aim of this paper is to present a new multi-objective technique which consists on a hybridization between a particle swarm optimization approach (Tribes) and tabu search technique. The main idea of the approach is to combine the high convergence rate of Tribes with a local search technique based on Tabu Search. Besides, in our study, we proposed different places to apply local search: the archive, the best particle among each tribe and each particle of the swarm. As a result of our study, we present three versions of our hybridized algorithm. The mechanisms proposed are validated using twelve different functions from specialized literature of multi-objective optimization. The obtained results show that using this kind of hybridization is justified as it is able to improve the quality of the solutions in the majority of cases.