Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems

IEEE Transactions on Evolutionary Computation

Volumn 19, Issue 2, 2015, Pages 264-283

  Ishibuchi, Hisao ^a   Akedo, Naoya ^a   Nojima, Yusuke ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

Evolutionary many objective optimization; evolutionary multiobjective optimization (EMO); many objective problems

Indexed keywords

ALGORITHMS; COMBINATORIAL OPTIMIZATION; EVOLUTIONARY ALGORITHMS;

DIVERSITY IMPROVEMENT; EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; HIGHLY-CORRELATED; MANY-OBJECTIVE OPTIMIZATIONS; MANY-OBJECTIVE PROBLEMS; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; PERFORMANCE DETERIORATION; SCALARIZING FUNCTION;

MULTIOBJECTIVE OPTIMIZATION;

EID: 84926357040     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2014.2315442     Document Type: Article

References (59)

1. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA, USA: Addison-Wesley, 1989.
2. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. Chichester, U.K.: Wiley, 2001.
3. C. A. C. Coello and G. B. Lamont, Applications of Multi-Objective Evolutionary Algorithms. Singapore: World Scientific, 2004.
4. K. C. Tan, E. F. Khor, and T. H. Lee, Multiobjective Evolutionary Algorithms and Applications. Berlin, Germany: Springer, 2005.
5. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, Apr. 2002.
6. E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the strength Pareto evolutionary algorithm," Comput. Eng. Netw. Lab. (TIK), Dept. Electric. Eng., ETH, Zurich, Switzerland, TIK-Rep. 103, 2001.
7. R. C. Purshouse and P. J. Fleming, "Evolutionary many-objective optimization: An exploratory analysis," in Proc. 2003 IEEE Congr. Evol. Comput., Canberra, ACT, Australia, pp. 2066-2073.
8. V. Khare, X. Yao, and K. Deb, "Performance scaling of multi-objective evolutionary algorithms," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Apr. 2003, pp. 376-390.
9. E. J. Hughes, "Evolutionary many-objective optimization: Many once or one many?" in Proc. 2005 IEEE Congr. Evol. Comput., Edinburgh, U.K., pp. 222-227.
10. H. Sato, H. E. Aguirre, and K. Tanaka, "Controlling dominance area of solutions and its impact on the performance of MOEAs," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Mar. 2007, pp. 5-20.
11. D. Corne and J. Knowles, "Techniques for highly multiobjective optimization: Some non-dominated points are better than others," in Proc. 2007 GECCO., London, U.K., pp. 773-780.
12. S. Kukkonen and J. Lampinen, "Ranking-dominance and many-objective optimization," in Proc. 2007 IEEE Congr. Evol. Comput., Singapore, pp. 3983-3990.
13. M. Köppen and K. Yoshida, "Substitute distance assignments in NSGAII for handling many-objective optimization problems," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Mar. 2007, pp. 727-741.
14. H. Singh, A. Isaacs, T. Ray, and W. Smith, "A study on the performance of substitute distance based approaches for evolutionary many objective optimization," in Simulated Evolution and Learning. Berlin, Germany: Springer, Dec. 2008, pp. 401-410.
15. Z. He, G. G. Yen, and J. Zhang, "Fuzzy-based Pareto optimality for many-objective evolutionary algorithms," IEEE Trans. Evol. Comput., vol. 18, no. 2, pp. 269-285, Apr. 2014.
16. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization: A short review," in Proc. 2008 IEEE Congr. Evol. Comput., Hong Kong, pp. 2424-2431.
17. H. Ishibuchi, N. Tsukamoto, Y. Hitotsuyanagi, and Y. Nojima, "Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems," in Proc. 2008 GECCO, Atlanta, GA, USA, pp. 649-656.
18. M. Li, S. Yang, and X. Liu, "Shift-based density estimation for Pareto-based algorithms in many-objective optimization," IEEE Trans. Evol. Comput., vol. 18, no. 3, pp. 348-365, June 2014.
19. S. Yang, M. Li, X. Liu, and J. Zheng, "A grid-based evolutionary algorithm for many-objective optimization," IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 721-736, Oct. 2013.
20. O. Schutze, 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. Evol. Comput., vol. 15, no. 4, pp. 444-455, Aug. 2011.
21. H. Ishibuchi, N. Akedo, H. Ohyanagi, and Y. Nojima, "Behavior of EMO algorithms on many-objective optimization problems with correlated objectives," in Proc. 2011 IEEE Congr. Evol. Comput., New Orleans, LA, USA, pp. 1465-1472.
22. H. Ishibuchi, N. Akedo, and Y. Nojima, "A many-objective test problem for visually examining diversity maintenance behavior in a decision space," in Proc. 2011 GECCO, Dublin, Ireland, pp. 649-656.
23. Q. Zhang and H. Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition," IEEE Trans. Evol. Comput., vol. 11, no. 6, pp. 712-731, Dec. 2007.
24. N. Beume, B. Naujoks, and M. Emmerich, "SMS-EMOA: Multiobjective selection based on dominated hypervolume," Eur. J. Oper. Res., vol. 181, no. 3, pp. 1653-1669, Sep. 2007.
25. J. Bader and E. Zitzler, "HypE: An algorithm for fast hypervolume-based many-objective optimization," Evol. Comput., vol. 19, no. 1, pp. 45-76, 2011.
26. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations,"in Proc. 2009 IEEE Int. Conf. SMC, San Antonio, TX, USA, pp. 1820-1825.
27. th Int. Conf. PPSN II, Taormina, Italy, Sep. 2012, pp. 132-142.
28. E. Zitzler and L. Thiele, "Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach," IEEE Trans. Evol. Comput., vol. 3, no. 4, pp. 257-271, Nov. 1999.
29. D. J. Walker, R. M. Everson, and J. E. Fieldsend, "Visualizing mutually nondominating solution sets in many-objective optimization," IEEE Trans. Evol. Comput., vol. 17, no. 2, pp. 165-184, Apr. 2013.
30. L. Thiele, K. Miettinen, P. J. Korhonen, and J. Molina, "A preference-based evolutionary algorithm for multi-objective optimization," Evol. Comput., vol. 17, no. 3, pp. 411-436, 2009.
31. I. Karahan and M. Koeksalan, "A territory defining multiobjective evolutionary algorithms and preference incorporation," IEEE Trans. Evol. Comput., vol. 14, no. 4, pp. 636-664, Aug. 2010.
32. T. Wagner and H. Trautmann, "Integration of preferences in hypervolume-based multiobjective evolutionary algorithms by means of desirability functions," IEEE Trans. Evol. Comput., vol. 14, no. 5, pp. 688-701, Oct. 2010.
33. J. H. Kim, J. H. Han, Y. H. Kim, S. H. Choi, and E. S. Kim, "Preference-based solution selection algorithm for evolutionary multiobjective optimization," IEEE Trans. Evol. Comput., vol. 16, no. 1, pp. 20-34, Feb. 2012.
34. R. Wang, R. C. Purshouse, and P. J. Fleming, "Preference-inspired coevolutionary algorithms for many-objective optimization," IEEE Trans. Evol. Comput., vol. 17, no. 4, pp. 474-494, Aug. 2013.
35. D. K. Saxena, J. A. Duro, A. Tiwari, K. Deb, and Q. Zhang, "Objective reduction in many-objective optimization: Linear and nonlinear algorithms,"IEEE Trans. Evol. Comput., vol. 17, no. 1, pp. 77-99, Feb. 2013.
36. O. Schütze, X. Esquivel, A. Lara, and C. A. C. Coello, "Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization," IEEE Trans. Evol. Comput., vol. 16, no. 4, pp. 504-522, Aug. 2012.
37. O. Schütze, X. Esquivel, A. Lara, and C. A. C. Coello, "Some comments on GD and IGD and relations to the Hausdorff distance," in Proc. GECCO, Portland, OR, USA, Jul. 2010, pp. 1971-1974.
38. I. Giagkiozis, R. C. Purshouse, and P. J. Fleming, "Generalized decomposition," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Mar. 2013, pp. 428-442.
39. th ICNC, Shanghai, China, Jul. 2011, pp. 1260-1264.
40. F. Gu, H.-L. Liu, and K. C. Tan, "A multiobjective evolutionary algorithm using dynamic weight design method," Int. J. Innov. Comput., Inf. Control, vol. 8, no. 5(B), pp. 3677-3688, May 2012.
41. Y. Qi et al., "MOEA/D with adaptive weight adjustment," Evol. Comput., vol. 22, no. 2, pp. 231-264, June 2014.
42. H.-L. Liu, F. Gu, and Q. Zhang, "Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems,"IEEE Trans. Evol. Comput., vol. 18, no. 3, pp. 450-455, June 2014.
43. T. Wagner, N. Beume, and B. Naujoks, "Pareto-, aggregation-, and indicator-based methods in many-objective optimization," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Mar. 2007, pp. 742-756.
44. L. While, L. Bradstreet, and L. Barone, "A fast way of calculating exact hypervolumes," IEEE Trans. Evol. Comput., vol. 16, no. 1, pp. 86-95, Feb. 2012.
45. L. M. S. Russo and A. P. Francisco, "Quick hypervolume," IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 481-502, Aug. 2014.
46. L. Judt, O. Mersmann, and B. Naujoks, "Do hypervolume regressions hinder EMOA performance? Surprise and relief," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Mar. 2013, pp. 96-110.
47. H. Ishibuchi, K. Narukawa, N. Tsukamoto, and Y. Nojima, "An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization," Eur. J. Oper. Res., vol. 188, no. 1, pp. 57-75, Jul. 2008.
48. H. Ishibuchi and K. Narukawa, "Recombination of similar parents in EMO algorithms," in Evolutionary Multi-Criterion Optimization. Berlin, Germany: Springer, Mar. 2005, pp. 265-279.
49. A. Moraglio and R. Poli, "Topological interpretation of crossover,"in Genetic and Evolutionary Computation. Berlin, Germany: Springer, Jun. 2004, pp. 1377-1388.
50. H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Diversity improvement by non-geometric binary crossover in evolutionary multiobjective optimization,"IEEE Trans. Evol. Comput., vol. 14, no. 6, pp. 985-998, Dec. 2010.
51. H. Sato, H. E. Aguirre, and K. Tanaka, "Genetic diversity and effective crossover in evolutionary many-objective optimization," in Learning and Intelligent Optimization. Berlin, Germany: Springer, Jan. 2011, pp. 91-105.
52. H. Ishibuchi, Y. Sakane, N. Tsukamoto, and Y. Nojima, "Simultaneous use of different scalarizing functions in MOEA/D," in Proc. GECCO, Portland, OR, USA, Jul. 2010, pp. 519-526.
53. S.-Z. Zhao, P. N. Suganthan, and Q. Zhang, "Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes," IEEE Trans. Evol. Comput., vol. 16, no. 3, pp. 442-446, Jun. 2012.
54. H. Ishibuchi and T. Murata, "A multi-objective genetic local search algorithm and its application to flowshop scheduling," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 28, no. 3, pp. 392-403, Aug. 1998.
55. H. Ishibuchi, T. Yoshida, and T. Murata, "Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling," IEEE Trans. Evol. Comput., vol. 7, no. 2, pp. 204-223, Apr. 2003.
56. K. Sindhya, K. Miettinen, and K. Deb, "A hybrid framework for evolutionary multi-objective optimization," IEEE Trans. Evol. Comput., vol. 17, no. 4, pp. 495-511, Aug. 2013.
57. L. Tang and X. Wang, "A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems," IEEE Trans. Evol. Comput., vol. 17, no. 1, pp. 20-45, Jan. 2013.
58. K. Deb and H. Jain, "An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: Solving problems with box constraints," IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 577-601, Aug. 2014.
59. H. Jain and K. Deb, "An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part II: Handling constraints and extending to an adaptive approach," IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 602-622, Aug. 2014.