Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Volumn , Issue , 2010, Pages 527-534

  Ishibuchi, Hisao ^a   Tsukamoto, Noritaka ^a   Sakane, Yuji ^a   Nojima, Yusuke ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

Evolutionary multiobjective optimization (EMO); Hypervolume; Indicator based evolutionary algorithm (IBEA); Many objectives

Indexed keywords

ALTERNATIVE FRAMEWORK; APPROXIMATION METHODS; COMPUTATION TIME; COMPUTATIONAL EXPERIMENT; EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; HYPERVOLUME; INDICATOR FUNCTIONS; INDICATOR-BASED EVOLUTIONARY ALGORITHM (IBEA); MANY OBJECTIVES; MULTI-OBJECTIVE PROBLEM; PARETO DOMINANCE; PERFORMANCE DETERIORATION; SCALARIZING FUNCTION; SOLUTION SET;

APPROXIMATION ALGORITHMS; APPROXIMATION THEORY; MULTIOBJECTIVE OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 77955888342     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830483.1830578     Document Type: Conference Paper

References (37)

1. Auger, A., Bader, J., Brockhoff, D., andZitzler, E. Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences. Proc. of 2009 Genetic and Evolutionary Computation Conference (2009) 563-570.
2. Auger, A., Bader, J., Brockhoff, D., andZitzler, E. Articulating user preferences in many-objective problems by sampling the weighted hypervolume. Proc. of 2009 Genetic and Evolutionary Computation Conference (2009) 555-562.
3. Bader, J., Deb, K., Zitzler, E. Faster hypervolume-based search using Monte Carlo sampling. Lecture Notes in Economics and Mathematical Systems 634: MCDM 2008, Springer, Berlin (2010) 313-326.
4. Beume, N. S-metric calculation by considering dominated hypervolume as Klee's measure problem. Evolutionary Computation 17, 4 (2009) 477-492.
5. Beume, N., Fonseca, C. M., Lopez-Ibanez, M., Paquete, L., andVahrenhold, J. On the complexity of computing the hypervolume indicator. IEEE Trans, on Evolutionary Computation 13, 5 (2009) 1075-1082.
6. Beume, N., Naujoks, B., andEmmerich, M. SMS-EMOA: multiobjective selection based on dominated hypervolume. European Journal of Operational Research 180, 3 (2007) 1653-1669.
7. Bradstreet, L., While, L., andBarone, L. A fast incremental hypervolume algorithm. IEEE Trans, on Evolutionary Computation 12, 6 (2008) 714-723.
8. Bringmann, K., andFriedrich, T. Approximating the least hypervolume contributor: NP-hard in general, but fast in practice. Lecture Notes in Computer Science 5467: Evolutionary Multi-Criterion Optimization - EMO 2009. Springer, Berlin (2009) 6-20.
9. Coello, C. A. C., andLamont, G. B. Applications of MultiObjective Evolutionary Algorithms. World Scientific, Singapore (2004).
10. Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. John Willy & Sons, Chichester (2001).
11. Deb, K., Pratap, A., Agarwal, S., andMeyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6,2 (2002) 182-197.
12. Deb, K., Thiele, L., Laumanns, M., andZitzler, E. Scalable multiobjective optimization test problems. Proc. of 2002 Congress on Evolutionary Computation (2002) 825-830.
13. Deb, K., Thiele, L., Laumanns, M., andZitzler, E. Scalable test problems for evolutionary multiobjective optimization. in Abraham, A., Jain, L. C., and Goldberg, R. (eds.), Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, Springer, Berlin (2005) 105-145.
14. Emmerich, M., Beume, N., andNaujoks, B. An EMO algorithm using the hypervolume measure as selection criterion. Lecture Notes in Computer Science 3410: Evolutionary Multi-Criterion Optimization - EMO 2005, Springer, Berlin (2005) 62-76.
15. Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989).
16. Hughes, E. J. Evolutionary many-objective optimization: Many once or one many?. Proc. of 2005 IEEE Congress on Evolutionary Computation (2005) 222-227.
17. Hughes, E. J. MSOPS-II: A general-purpose many-objective optimizer. Proc. of 2007 IEEE Congress on Evolutionary Computation (2007) 3944-3951.
18. Ishibuchi, H., Tsukamoto, N., Hitotsuyanagi, Y., andNojima, Y. Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. Proc. of 2008 Genetic and Evolutionary Computation Conference (2008) 649-656.
19. Ishibuchi, H., Tsukamoto, N., Sakane, Y., and Nojima, Y., Hypervolume approximation using achievement scalarizing functions for evolutionary many-objective optimization. Proc. of 2009 IEEE Congress on Evolutionary Computation (2009) 530-537.
20. Jaszkiewicz, A. On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study. European Journal of Operational Research 158, 2 (2004) 418-433.
21. Khara, V., Yao, X., andDeb, K. Performance scaling of multi-objective evolutionary algorithms. Lecture Notes in Computer Science 2632: Evolutionary Multi-Criterion Optimization - EMO 2003, Springer, Berlin (2004) 367-390.
22. Li, H., andZhang, Q. Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. on Evolutionary Computation 13, 2 (2009) 284-302.
23. Miettinen, K. Nonlinear Multiobjective Optimization. Kluwer, Boston (1998).
24. Miettinen, K., and Makela, M. On scalarizing functions in multiobjective optimization. OR Spectrum 24,2 (2002) 193-213.
25. Murata, T., Ishibuchi, H., andGen, M. Specification of genetic search directions in cellular multi-objective genetic algorithm, Lecture Notes in Computer Science 1993: Evolutionary Multi-Criterion Optimization - EMO 2001, Springer, Berlin (2001), 82-95.
26. Purshouse, R. C. andFleming, P. J. On the evolutionary optimization of many conflicting objectives. IEEE Trans. on Evolutionary Computation 11, 6 (2007) 770-784.
27. Tan, K. C., Khor, E. F., andLee, T. H. Multiobjective Evolutionary Algorithms and Applications, Springer, Berlin (2005).
28. Wagner, T., Beume, N., andNaujoks, B. Pareto-, aggregation-and indicator-based methods in many-objective optimization. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007), 742-756.
29. While, L., Hingston, P., Barone, L., andHuband, S. A faster algorithm for calculating hypervolume. IEEE Trans. on Evolutionary Computation 10, 1 (2006) 29-38.
30. Wierzbicki, A. P., Makowski, M., andWessels, J. (eds.), Model-Based Decision Support Methodology with Environmental Applications, Kluwer, Dordrecht (2000).
31. Zhang, Q., andLi, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation 11, 6 (2007) 712-731.
32. Zitzler, E., Brockhoff, D., andThiele, L. The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration. Lecture Notes in Computer Science 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 862-876.
33. Zitzler, E., andKünzli, S. Indicator-based selection in multiobjective search. Proc. of PPSN 2004, 832-842.
34. Zitzler, E., and Laumanns, M., andThiele, L. SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-Report, No. 103 (2001).
35. Zitzler, E., andThiele, L. Multiobjective optimization using evolutionary algorithms - A comparative case study. Lecture Notes in Computer Science 1498: Parallel Problem from Solving Nature-PPSN V, Springer, Berlin (1998) 292-301.
36. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., andda Fonseca, V. G. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Trans. on Evolutionary Computation 7,2 (2003) 117-132.
37. Zou, X., Chen, Y., Liu, M., andKang, L. A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans. on Systems, Man, and Cybernetics: Part B Cybernetics 38, 5 (2008) 1402-1412.