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Solving Hard Multiobjective Problems with a Hybridized Method
Keywords:
Particle Swarm Optimization, Multi-objective Optimization, Epsilon-constraint MethodAbstract
This paper presents a hybrid method to solve hard multiobjective problems. The proposed approach adopts an epsilon-constraint method which uses a Particle Swarm Optimizer to get points near of the true Pareto front. In this approach, only few points will be generated and then, new intermediate points will be calculated using an interpolation method, to increase the among of points in the output Pareto front. The proposed approach is validated using two difficult multiobjective test problems and the results are compared with those obtained by a multiobjective evolutionary algorithm representative of the state of the art: NSGA-II.
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References
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[17] Y. Wang and Z. Cai. A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems. Frontiers of Computer Science in China, 2009.
[18] Y. Wang and Y. Yang. Particle swarm optimization with preference order ranking for multi-objective optimization. Inf. Sci., 179(12):1944–1959, 2009.
[19] E. Zahara and C. Hu. Solving constrained optimization problems with hybrid particle swarm optimization. Engineering Optimization, 40, 2008.
[20] E. Zitzler, K. Deb, and L. Thiele. Comparison of the multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173–195, 2000.
[2] C. Coello Coello, G. Lamont, and D. Van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Springer, 2007. ISBN 978-0-387-33254-3.
[3] J. L. Cohon and D. H. Marks. A review and evaluation of multiobjective programming techniques. Water Resources Research, 11(2):208–220, 1975.
[4] K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.
[5] R. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS’95, pages 39–43, Nagoya, Japan, October 1995. IEEE Press.
[6] J. Grobler and A. P. Engelbrecht. Hybridizing PSO and DE for improved vector evaluated multi-objective optimization. E-Commerce Technology, IEEE International Conference on, 0:1255–1262, 2009.
[7] Y. Y. Haimes, L. S. Lasdon, and D. A. Wismer. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transaction on Systems, Man, and Cybernetics, 1(3):296–297, 1971.
[8] J. Horn. Multicriterion decision making. Handbook of Evolutionary Computation, 1:F1.9:1–F1.9:15, 1997.
[9] W. Jingxuan and W. Yuping. Multi-objective fuzzy particle swarm optimization based on elite archiving and its convergence. Journal of Systems Engineering and Electronics, 19(5):1035–1040, 2008.
[10] X. Lin and H. Li. Enhanced pareto particle swarm approach for multi-objective optimization of surface grinding process. 2007 Workshop on Intelligent Information Technology Applications, 2:618–623, 2008.
[11] C. Liu. New dynamic constrained optimization pso algorithm. In ICNC ’08: Proceedings of the 2008 Fourth International Conference on Natural Computation, pages 650–653, Washington, DC, USA, 2008. IEEE Computer Society.
[12] S. McKinley and M. Levine. Cubic spline interpolation. Math 45: Linear Algebra.
[13] K. M. Miettinen. Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, 1999. Boston, Massachusetts.
[14] T. Okabe. Evolutionary Multi-Objective Optimization - On the Distribution of Offspring in Parameter and Fitness Space. PhD thesis, Bielefeld University, 2004.
[15] D. A. Van Veldhuizen and G. B. Lamont. Multiobjective evolutionary algorithm research: A history and analysis. Technical report, Dept. Elec. Comput. Eng., Graduate School of Eng., Air Force Inst. Technol, Wright-Patterson, AFB.OH, 1998. TR-98-03.
[16] D. A. Van Veldhuizen and G. B. Lamont. Multiobjective evolutionary algorithms: Analysing the state-of-the-art. Evolutionary Computation, 7(3):1–26, 2000.
[17] Y. Wang and Z. Cai. A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems. Frontiers of Computer Science in China, 2009.
[18] Y. Wang and Y. Yang. Particle swarm optimization with preference order ranking for multi-objective optimization. Inf. Sci., 179(12):1944–1959, 2009.
[19] E. Zahara and C. Hu. Solving constrained optimization problems with hybrid particle swarm optimization. Engineering Optimization, 40, 2008.
[20] E. Zitzler, K. Deb, and L. Thiele. Comparison of the multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173–195, 2000.
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Published
2010-10-01
How to Cite
Cagnina, L., & Esquivel, S. C. (2010). Solving Hard Multiobjective Problems with a Hybridized Method. Journal of Computer Science and Technology, 10(03), p. 117–122. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/698
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