@article{(Open Science Index):https://publications.waset.org/pdf/10009813,
	  title     = {An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems},
	  author    = {Houda Abadlia and  Nadia Smairi and  Khaled Ghedira},
	  country	= {},
	  institution	= {},
	  abstract     = {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.
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {12},
	  number    = {11},
	  year      = {2018},
	  pages     = {1010 - 1018},
	  ee        = {https://publications.waset.org/pdf/10009813},
	  url   	= {https://publications.waset.org/vol/143},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 143, 2018},