Using archiving methods to control convergence and diversity for Many-Objective Problems in Particle Swarm Optimization

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Britto, Andre ^a   Pozo, Aurora ^a  

^a FEDERAL UNIVERSITY OF PARANÁ   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL ANALYSIS; METAHEURISTIC; MULTI OBJECTIVE; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; OBJECTIVE FUNCTIONS; PARETO FRONT; SWARM INTELLIGENCE;

ARTIFICIAL INTELLIGENCE; PARTICLE SWARM OPTIMIZATION (PSO);

MULTIOBJECTIVE OPTIMIZATION;

EID: 84866874998     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256149     Document Type: Conference Paper

References (20)

1. C. A. C. Coello, G. B. Lamont, and D. A. V. Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.
2. M. Reyes-Sierra and C. A. C. Coello, "Multi-objective particle swarm optimizers: A survey of the state-of-the-art," International Journal of Computational Intelligence Research, vol. 2, no. 3, pp. 287-308, 2006.
3. M. López-Ibáñez, J. Knowles, and M. Laumanns, "On sequential online archiving of objective vectors," in Evolutionary Multi-Criterion Optimization, ser. Lecture Notes in Computer Science, R. Takahashi, K. Deb, E. Wanner, and S. Greco, Eds. Springer Berlin / Heidelberg, 2011, vol. 6576, pp. 46-60.
4. A. Nebro, J. Durillo, J. Garcia-Nieto, C. A. C. Coello, F. Luna, and E. Alba, "SMPSO: A new pso-based metaheuristic for multi-objective optimization," in IEEE symposium on Computational intelligence in miulti-criteria decision-making, 2009. mcdm '09, 2009, pp. 66-73.
5. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization: A short review," in CEC 2008. IEEE Congress on Evolutionary Computation, 2008, pp. 2419-2426.
6. O. Schütze, A. Lara, and C. A. C. Coello, "On the influence of the number of objectives on the hardness of a multiobjective optimization problem," IEEE Trans. Evolutionary Computation, vol. 15, no. 4, pp. 444-455, 2011.
7. H. Ishibuchi, N. Akedo, H. Ohyanagi, and Y. Nojima, "Behavior of emo algorithms on many-objective optimization problems with correlated objectives," in Evolutionary Computation (CEC), 2011 IEEE Congress on, june 2011, pp. 1465 -1472.
8. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, "Scalable multiobjective optimization test problems," in Congress on Evolutionary Computation (CEC 2002), 2002, pp. 825-830.
9. A. L. Jaimes and C. A. C. Coello, "Study of preference relations in many-objective optimization," Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09, pp. 611-618, 2009.
10. A. B. d. Carvalho and A. Pozo, "Measuring the convergence and diversity of cdas multi-objective particle swarm optimization algorithms: A study of many-objective problems," Neurocomputing, vol. 75, pp. 43-51, Jan. 2012.
11. R. Purshouse, C. Jalba, and P. Fleming, "Preference-driven co-evolutionary algorithms show promise for many-objective optimisation," in Evolutionary Multi-Criterion Optimization, ser. Lecture Notes in Computer Science, R. Takahashi, K. Deb, E. Wanner, and S. Greco, Eds. Springer Berlin / Heidelberg, 2011, vol. 6576, pp. 136-150.
12. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, August 2002.
13. D. Corne and J. Knowles, "Some multiobjective optimizers are better than others," in Evolutionary Computation, 2003. CEC '03. The 2003 Congress on, vol. 4, dec. 2003, pp. 2506-2512 Vol.4.
14. M. Laumanns, L. Thiele, K. Deb, and E. Zitzler, "Combining convergence and diversity in evolutionary multiobjective optimization," Evol. Comput., vol. 10, pp. 263-282, September 2002.
15. J. D. Knowles and D. W. Corne, "Approximating the nondominated front using the pareto archived evolution strategy," Evol. Comput., vol. 8, pp. 149-172, 2000.
16. M. Laumanns and R. Zenklusen, "Stochastic convergence of random search methods to fixed size pareto front approximations," European Journal of Operational Research, vol. 213, no. 2, pp. 414-421, 2011.
17. M. Garza-Fabre, G. T. Pulido, and C. A. Coello, "Ranking methods for many-objective optimization," in Proceedings of the 8th Mexican International Conference on Artificial Intelligence, ser. MICAI '09. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 633-645.
18. C. M. Fonseca, L. Paquete, and M. López-Ibáñez, "An improved dimension-sweep algorithm for the hypervolume indicator," in Proceedings of the Evolutionary Computation on 2006. CEC '06. Proceedings of the 2006 Congress, ser. CEC '06, 2006, pp. 1157-1163.
19. J. Demšar, "Statistical comparisons of classifiers over multiple data sets," The Journal of Machine Learning Research, vol. 7, pp. 1-30, 2006.
20. T. Wagner, N. Beume, and B. Naujoks, "Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization," 2007, pp. 742-756.