Reference point based multi-objective optimization through decomposition

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Mohammadi, Asad ^a   Omidvar, Mohammad Nabi ^a   Li, Xiaodong ^a  

^a RMIT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFORT; COMPUTATIONAL RESOURCES; DECISION MAKERS; DECOMPOSITION METHODS; DECOMPOSITION STRATEGY; EVOLUTIONARY MULTI-OBJECTIVE ALGORITHMS; FOCUSED SEARCH; HIGH-QUALITY SOLUTIONS; MULTI OBJECTIVE OPTIMIZATIONS (MOO); MULTI-OBJECTIVE PROBLEM; REFERENCE POINTS; SELECTION PRESSURES; WEIGHT VECTOR;

MULTIOBJECTIVE OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 84866867193     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256486     Document Type: Conference Paper

References (27)

1. K. Deb, Multi-Objective Optimization using Evolutionary Algorithm. Wiley, 2008.
2. C. A. C. Coello and G. B. Lamont, Eds., Applications Of Multi-Objective Evolutionary Algorithms. World Scientific, 2004.
3. 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.
4. F. di Pierro, Many-Objective Optimization and Applications to Water Engineering: Theory and Practice. LAP Lambert Academic Publishing, April 2010.
5. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization: A short review," in Proc of 2008 IEEE Congress on Evolutionary Computation, 2008, pp. 2419-2426.
6. -, "Evolutionary many-objective optimization," in Proc. of 3rd International Workshop on Genetic and Evolving Fuzzy Systems, no. March, 2008, pp. 47-52.
7. K. Deb, J. Sundar, U. B. R. N, and S. Chaudhuri, "Reference point based multi-objective optimization using evolutionary algorithm," International Journal of Computational Intelligence Research, pp. 273-286, 2006.
8. K. Deb, a. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Transactions on Evolutionary Computation, pp. 182-197, 2002.
9. E. Zitzler and L. Thiele, "Multi objective evolutionary algorithms: A comparative case study and the strength pareto approach," Evolutionary Computation, IEEE Transactions on, vol. 3, no. 4, pp. 257-271, 1999.
10. T. Murata and H. Ishibuchi, "Moga: multi-objective genetic algorithms," in Evolutionary Computation, 1995., IEEE International Conference on, vol. 1, nov-1 dec 1995, p. 289.
11. E. J. Hughes, "Evolutionary many-objective optimisation: many once or one many?" in Proc. of 2005 IEEE Congress on Evolutionary Computation, 2005, pp. 222-227.
12. K. Miettinen, Nonlinear Multiobjective Optimization. Norwell, MA: Kluwer, 1999.
13. H. Ishibuchi and T. Murata, "A multi-objective genetic local search algorithm and its application to flowshop scheduling," IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), pp. 392-403, 1998.
14. Q. Zhang and H. Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition," Evolutionary Computation, IEEE Transactions, pp. 712-731, 2007.
15. C. Coello, "Handling preferences in evolutionary multiobjective optimization: a survey," in Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, vol. 1, 2000, pp. 30-37.
16. A. P. Wierzbicki, "The use of reference objectives in multiobjective optimization," Multiple Criteria Decision Making Theory and Applications, pp. 468-486, 1980.
17. W. U.K. and X. Li, "Integrating user preferences with particle swarms for multi-objective optimization," in Proceedings of the 10th annual conference on Genetic and evolutionary computation. ACM, 2008, pp. 745-752.
18. K. Deb, "Solving goal programming problems using multi-objective genetic algorithms," in Proceedings of the 1999 Congress on Evolutionary Computation, 1998.
19. J. P. Ignizio, Goal Programming and Extensions. Lexington Books, 1976.
20. D. Cvetkovic and I. Parmee, "Evolutionary design and multi-objective optimisation," in In proc. of 6th European Congress on Intelligent Techniques and Soft Computing EUFIT'98, 1998, pp. 397-401.
21. Y. Jin and B. Sendhoff, "Incorporation of fuzzy preferences into evolutionary multiobjective optimization," in 4th Asia-Pacific Conference in Simulated Evolution and Learning, 2002, pp. 26-30.
22. J. Branke, T. Kaubler, and H. Schmeck, "Guidance in evolutionary multi-objective optimization," Advances in Engineering Software, pp. 499-507, 2001.
23. K. Deb, "Multi-objective evolutionary algorithms : Introducing bias among pareto-optimal solutions multi-objective optimization," Optimization, pp. 263-292, 2003.
24. E. Zitzler, K. Deb, and L. Thiele, "Comparison of multiobjective evolutionary algorithms: empirical results." Evolutionary computation, pp. 173-95, 2000.
25. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, "Scalable test problems for evolutionary multi-objective optimization," Kanpur Genetic Algorithms Laboratory, Tech. Rep., 2001.
26. R. Storn and K. Price, "Differential evolution . a simple and efficient heuristic for global optimization over continuous spaces," Journal of Global Optimization 11 (4), pp. 341-359, 1995.
27. D. V. Veldhuizen, "Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations," Ph.D. dissertation, OH: Air Force Institute of Technology, Dayton, 1999, Technical Report No. AFIT/DS/ENG/99-01.