TY - JFULL AU - Houda Abadlia and Nadia Smairi and Khaled Ghedira PY - 2018/12/ TI - An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems T2 - International Journal of Computer and Information Engineering SP - 1009 EP - 1018 VL - 12 SN - 1307-6892 UR - https://publications.waset.org/pdf/10009813 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 143, 2018 N2 - Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems. ER -