Abstract
This paper presents a new multi-objective technique which consists of a hybrid between a particle swarm optimization approach (PSO) and tabu search (TS) technique. The main idea of the approach is to combine the high convergence rate of PSO with a local search technique based on Tabu Search. Besides, in our study, we proposed to apply local search to improve the capacity of exploitation of PSO. The mechanisms proposed are validated using fifteen 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.