An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2021953Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 383
 CA. Coello, DA. Veldhuizen and GB. Lamont. Evolutionary algorithms for solving multi-objective problems. IEEE, 2002.
 J. Kennedy and R. C. Eberhart. Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, IEEE Press. pp. 1942-1948, Perth, Australia, 1995.
 C. A. Coello Coello, G. T. Pulido, M. S. Lechuga, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput. 8, pp 256–279, 2004.
 P. K. Tripathi, S. Bandyopadhya and S. K. Pal. Adaptive Multi-objective Particle Swarm Optimization Algorithm. IEEE Congress on Evolutionary Computation,2007.
 J. Teich Mostaghim. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO), in: IEEE 2003 Swarm Intelligence Symposium, 2003.
 Z.-H. Liu, J. Zhang, S.-W. Zhou, X.-H. Li, and K. Liu, “Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM,” IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1921–1935, 2013.
 Z-H. Zhan and J. Zhang, Discrete Particle Swarm Optimization for Multiple Destination Routing Problems, EvoWorkshops, LNCS 5484, Springer, 2009, pp. 117-122.
 K. E. Parspoulos. Parallel cooperative micro-particle swarm optimization: A master salve model. Journal of Applied Soft Computing, volume12, pp 3552-3579, 2012.
 H. Abadlia, N. Smairi and K. Ghedira. A new proposal for a multi-objective technique using SMPSO and Tabu Search. 15thIEEE/ACIS International Conference on Computer and Information Science, pp 1-6, Japan, 2016.
 H. T. T. Thein. Island model based differential evolution algorithm for neural network training. Advances in Computer Science: An International Journal, 3(1), 2014.
 R. Michel and M. Middendorf. An island model based ant system with look ahead for the shortest super sequence problem. In Parallel problem solving from nature PPSN V (pp. 692–701). Springer, 1998.
 M. Tomassini. Spatially structured evolutionnary algorithms: Artificial evolution in space time. Secaus, NJ, USA: Spring-Verlag New York, 2005.
 F. Lardeux and A. Goeffon. A Dynamic Island-Based Genetic Algorithms Framework. SEAL '10: 156-165, 2010.
 C. Candan, A. Goeffon, F. Lardeux and F. Saubien. A Dynamic Island Model for Adaptive Operator Selection. GECCO'12, 2012.
 P. Hansen and N. Mladenovic. An introduction to variable neighborhood search. Springer, 1999.
 M. Reyes Sierra and C. A. Coello Coello. Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ǫ-Dominance. In Evolutionary MultiCriterion Optimization (EMO 2005), LNCS 3410, pages 505–519, 2005.
 J. J. Durillo, J. García-Nieto, A. J. Nebro, C. A. C. Coello, F. Luna and E. Alba. Multi-Objective Particle Swarm Optimizers: An Experimental Comparison. 5th International Conference, Nantes, France, pp.495-509, 2009.
 K. Deb, S. Agarwal, A. Pratap, and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6(2), pp. 182–197, 2002.
 E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173–195, 2000.
 K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable Test Problems for Evolutionary Multiobjective Optimization. In A. Abraham, L. Jain, and R. Goldberg, editors, Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, pages 105–145. Springer, 2005.
 K. Deb. Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken, 2001.
 Knowles J, Thiele L, Zitzler E. A tutorial on the performance assessment of stochastic multiobjective optimizers. Tech. Rep. 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, 2006.
 J. J. Durillo, A. J. Nebro and E. Alba. jMetal framework for multiobjective optimization: design and architecture. In: IEEE conference on evolutionary computation CEC-2010, pp 4138– 4325.