Evolutionary algorithms with segment-based search for multiobjective optimization problems

IEEE Transactions on Cybernetics

Volumn 44, Issue 8, 2014, Pages 1295-1313

  Li, Miqing ^a   Yang, Shengxiang ^b   Li, Ke ^c   Liu, Xiaohui ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

^b DE MONTFORT UNIVERSITY   (United Kingdom)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Hybrid evolutionary algorithms; multiobjective optimization; segment based search; variation operators

Indexed keywords

ALGORITHMS;

EVOLUTIONARY INFORMATION; EXPERIMENTAL VERIFICATION; HYBRID EVOLUTIONARY ALGORITHM; INFORMATION EXCHANGES; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; RELAXATION STRATEGIES; SEGMENT-BASED; VARIATION OPERATOR;

MULTIOBJECTIVE OPTIMIZATION;

ALGORITHM; BIOLOGICAL MODEL; INFORMATION SCIENCE; PROCEDURES;

ALGORITHMS; INFORMATION SCIENCE; MODELS, BIOLOGICAL;

EID: 84904706204     PISSN: 21682267     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCYB.2013.2282503     Document Type: Article

References (65)

1. S. F. Adra, T. J. Dodd, I. A. Griffin, and P. J. Fleming, "Convergence acceleration operator for multiobjective optimization," IEEE Trans. Evol. Comput., vol. 13, no. 4, pp. 825-847, Aug. 2009.
2. 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. (Pubitemid 46498552)
3. P. Bosman, "On gradients and hybrid evolutionary algorithms for realvalued multiobjective optimization," IEEE Trans. Evol. Comput., vol. 16, no. 1, pp. 51-69, Feb. 2012.
4. T. Chan, K. Man, K. Tang, and S. Kwong, "A jumping gene paradigm for evolutionary multiobjective optimization," IEEE Trans. Evol. Comput., vol. 12, no. 2, pp. 143-159, Apr. 2008. (Pubitemid 351546691)
5. X. Chen, Y. S. Ong, M. Lim, and K. C. Tan, "A multi-facet survey on memetic computation," IEEE Trans. Evol. Comput., vol. 15, no. 5, pp. 591-607, Oct. 2011.
6. 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. (Pubitemid 34555372)
7. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. New York, NY, USA: Wiley, 2001.
8. J. J. Durillo, A. J. Nebro, F. Luna, and E. Alba, "On the effect of the steady-state selection scheme in multi-objective genetic algorithms," in Proc. 5th Int. Conf. Evol. Multi-Criterion Optimiz., 2009, pp. 183-197.
9. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, "Scalable test problems for evolutionary multiobjective optimization," in Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, A. Abraham, L. Jain, and R. Goldberg, Eds. Berlin, Germany: Springer, 2005, pp. 105-145.
10. A. Elhossini, S. Areibi, and R. Dony, "Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization," Evol. Comput., vol. 18, no. 1, pp. 127-156, 2010.
11. M. Emmerich, A. Deutz, and N. Beume, "Gradient-based/evolutionary relay hybrid for computing Pareto front approximations maximizing the S-metric," in Hybrid Metaheuristics, T. Bartz-Beielstein, M. J. Blesa, C. Blum, B. Naujoks, A. Roli, G. Rudolph, and M. Sampels, Eds. Berlin, Germany: Springer, 2007, pp. 140-156.
12. H. Fan and J. Lampinen, "A trigonometric mutation operation to differential evolution," J. Global Optim., vol. 27, no. 1, pp. 105-129, Sep. 2003.
13. C. M. Fonseca and P. J. Fleming, "On the performance assessment and comparison of stochastic multiobjective optimizers," in Proc. Parallel Problem Solv. Nature, 1996, pp. 584-593. (Pubitemid 126128346)
14. D. Garrett and D. Dasgupta, "An empirical comparison of memetic algorithm strategies on the multiobjective quadratic assignment problem," in Proc. IEEE Symp. Comput. Intell. Multi-Criteria Decision-Mak., 2009, pp. 80-87.
15. C. K. Goh, Y. S. Ong, and K. C. Tan, Multi-Objective Memetic Algorithms. Berlin, Germany: Springer Verlag, 2009.
16. C. Grosan and A. Abraham, "Hybrid evolutionary algorithms: Methodologies, architectures, and reviews," in Hybrid Evolutionary Algorithms. Berlin, Germany: Springer, 2007, pp. 1-17.
17. C. Grosan, A. Abraham, and H. Ishibuchi, Hybrid Evolutionary Algorithms. Berlin, Germany: Springer-Verlag, 2007.
18. F. Herrera, M. Lozano, and J. Verdegay, "Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis," Artif. Intell. Review, vol. 12, no. 4, pp. 265-319, Aug. 1998. (Pubitemid 128514089)
19. S. Huband, P. Hingston, L. Barone, and L. While, "A review of multiobjective test problems and a scalable test problem toolkit," IEEE Trans. Evol. Comput., vol. 10, no. 5, pp. 477-506, Oct. 2006. (Pubitemid 44560739)
20. C. Igel, N. Hansen, and S. Roth, "Covariance matrix adaptation for multi-objective optimization," Evol. Comput., vol. 15, no. 1, pp. 1-28, 2007.
21. H. Ishibuchi and T. Murata, "Multi-objective genetic local search algorithm," in Proc. IEEE Int. Conf. Evol. Comput., 1996, pp. 119-124.
22. A. Jaszkiewicz, "On the performance of multiple-objective genetic local search on the 0/1 knapsack problem-A comparative experiment," IEEE Trans. Evol. Comput., vol. 6, no. 4, pp. 402-412, Aug. 2002. (Pubitemid 35032741)
23. M. Kleeman, G. Lamont, A. Cooney, and T. Nelson, "A multi-tiered memetic multiobjective evolutionary algorithm for the design of quantum cascade lasers," in Proc. Evol. Multi-Criterion Optimization, 2007, pp. 186-200. (Pubitemid 350270886)
24. J. Knowles, "A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers," in Proc. 5th Int. Conf. on Intell. Syst. Design Appl., 2005, pp. 552-557.
25. J. Knowles and D. Corne, "Memetic algorithms for multiobjective optimization: Issues, methods and prospects," Recent Advances in Memetic Algorithms. Berlin, Germany: Springer, 2005, pp. 313-352.
26. J. Knowles and D. Corne, "M-PAES: A memetic algorithm for multiobjective optimization," in Proc. Congr. Evol. Comput., vol. 1, 2000, pp. 325-332.
27. P. Koch, O. Kramer, G. Rudolph, and N. Beume, "On the hybridization of SMS-EMOA and local search for continuous multiobjective optimization," in Proc. 11th Annu. Conf. Genetic Evol. Comput., 2009, pp. 603-610.
28. N. Krasnogor and J. Smith, "A tutorial for competent memetic algorithms: Model, taxonomy, and design issues," IEEE Trans. Evol. Comput., vol. 9, no. 5, pp. 474-488, Oct. 2005. (Pubitemid 41522757)
29. S. Kukkonen and K. Deb, "A fast and effective method for pruning of non-dominated solutions in many-objective problems. " in Proc. 9th Int. Conf. Parallel Problem Solving from Nature, 2006, pp. 553-562. (Pubitemid 44609431)
30. A. Lara, G. Sanchez, C. Coello Coello, and O. Schütze, "HCS: A new local search strategy for memetic multiobjective evolutionary algorithms," IEEE Trans. Evol. Comput., vol. 14, no. 1, pp. 112-132, Feb. 2010.
31. C. Lo and W. Chang, "A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 30, no. 3, pp. 461-470, Jun. 2000.
32. M. Lozano, F. Herrera, N. Krasnogor, and D. Molina, "Real-coded memetic algorithms with crossover hill-climbing," Evol. Comput., vol. 12, no. 3, pp. 273-302, 2004. (Pubitemid 39212980)
33. M. Li, S. Yang, and X. Liu, "Shift-based density estimation for Paretobased algorithms in many-objective optimization," IEEE Trans. Evol. Comput., 2013. In press (DOI: 10.1109/TEVC.2013.2262178).
34. M. Li, S. Yang, X. Liu, and R. Shen, "A comparative study on evolutionary algorithms for many-objective optimization," in Proc. Evol. Multi-Criterion Optimiz., 2013, pp. 261-275.
35. M. Li, S. Yang, J. Zheng, and X. Liu, "ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization," Evol. Comput., 2013. In press (DOI: 10.1162/EVCO a 00106).
36. S. Martinez and C. A. C. Coello, "A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms," in Proc. IEEE Congr. Evol. Comput., 2012, pp. 1-8.
37. Y. Mei, K. Tang, and X. Yao, "A memetic algorithm for periodic capacitated arc routing problem," IEEE Trans. Syst. Man, Cybern. B, Cybern., vol. 41, no. 6, pp. 1654-1667, Dec. 2011.
38. H. Mühlenbein and D. Schlierkamp-Voosen, "Predictive models for the breeder genetic algorithm i. continuous parameter optimization," Evol. Comput., vol. 1, no. 1, pp. 25-49, 1993.
39. K. Nawaz Ripon, S. Kwong, and K. Man, "A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization," Inform. Sci., vol. 177, no. 2, pp. 632-654, Jan. 2007. (Pubitemid 44634453)
40. A. J. Nebro, F. Luna, E. Alba, B. Dorronsoro, J. J. Durillo, and A. Beham, "AbYSS: Adapting scatter search to multiobjective optimization," IEEE Trans. Evol. Comput., vol. 12, no. 4, pp. 439-457, Aug. 2008.
41. Y. S. Ong, M. Lim, and X. Chen, "Memetic computation-Past, present & future," IEEE Comput. Intell. Mag., vol. 5, no. 2, pp. 24-31, May 2010.
42. S. Rahnamayan, H. Tizhoosh, and M. Salama, "Opposition versus randomness in soft computing techniques," Appl. Soft Comput., vol. 8, no. 2, pp. 906-918, 2008. (Pubitemid 350266486)
43. S. Rahnamayan, H. Tizhoosh, and M. Salama, "Opposition-based differential evolution," IEEE Trans. Evol. Comput., vol. 12, no. 1, pp. 64-79, Feb. 2008. (Pubitemid 351330332)
44. J. D. Schaffer, "Multiple objective optimization with vector evaluated genetic algorithms," in Proc. 1st Int. Conf. Genetic Algorithms, 1985, pp. 93-100.
45. O. Schütze, C. A. C. Coello, S. Mostaghim, E. G. Talbi, and M. Dellnitz, "Hybridizing evolutionary strategies with continuation methods for solving multi-objective problems," Eng. Optim., vol. 40, no. 5, pp. 383-402, May 2008. (Pubitemid 351598806)
46. 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. Evol. Comput., vol. 15, no. 4, pp. 444-455, Aug. 2011.
47. K. Sindhya, K. Deb, and K. Miettinen, "A local search based evolutionary multi-objective optimization approach for fast and accurate convergence," in Proc. 10th Int. Conf. Parallel Problem Solv. Nature, 2008, pp. 815-824.
48. A. Sinha and D. Goldberg, "A survey of hybrid genetic and evolutionary algorithms," IlliGAL Rep., vol. 2003004, 2003.
49. J. Smith, "Coevolving memetic algorithms: A review and progress report," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 1, pp. 6-17, Feb. 2007. (Pubitemid 46358475)
50. O. Soliman, L. Bui, and H. Abbass, "A memetic coevolutionary multiobjective differential evolution algorithm," in Multi-Objective Memetic Algorithms. Berlin, Germany: Springer, 2009, pp. 369-388.
51. K. C. Tan, Y. J. Yang, C. K. Goh, and T. H. Lee, "Enhanced distribution and exploration for multiobjective evolutionary algorithms," in Proc. Congr. Evol. Comput., vol. 4, 2003, pp. 2521-2528.
52. K. C. Tan, S. C. Chiam, A. A. Mamun, and C. K. Goh, "Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization," Eur. J. Oper. Res., vol. 197, no. 2, pp. 701-713, Sep. 2009.
53. H. Tizhoosh, "Opposition-based learning: A new scheme for machine intelligence," in Proc. Int. Conf. Comput. Intell. Model. Control Autom., vol. 1, 2005, pp. 695-701.
54. D. A. Van Veldhuizen, "Multiobjective evolutionary algorithms: Classifications, analyses, and new innovations," Ph.D. dissertation, Dept. Electr. Comput. Eng., Air Force Inst. Technol., Wright-Patterson AFB, Ohio, OH, USA, 1999.
55. M. Vasile and M. Locatelli, "A hybrid multiagent approach for global trajectory optimization," J. Global Optim., vol. 44, no. 4, pp. 461-479, Aug. 2009.
56. T. Voß, N. Hansen, and C. Igel, "Recombination for learning strategy parameters in the MO-CMA-ES," in Proc. Evol. Multi-Criterion Optimiz., 2009, pp. 155-168.
57. Y. Wang, Z. Cai, G. Guo, and Y. Zhou, "Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 3, pp. 560-575, Jun. 2007. (Pubitemid 46784592)
58. E. Wanner, F. Guimarães, R. Takahashi, and P. Fleming, "Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria," Evol. Comput., vol. 16, no. 2, pp. 185-224, 2008.
59. S. Yang and S. Jat, "Genetic algorithms with guided and local search strategies for university course timetabling," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 1, pp. 93-106, Feb. 2011.
60. S. Yang and C. Li, "A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments," IEEE Trans. Evol. Comput., vol. 14, no. 6, pp. 959-974, Dec. 2010.
61. 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.
62. Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, "Multiobjective optimization test instances for the CEC 2009 special session and competition," Univ. Essex, Essex, U.K., Work. Rep. CES- 487, 2009.
63. 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. (Pubitemid 350187486)
64. E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization," in Proc. Evol. Methods Design, Optimiz. Control, 2002, pp. 95-100.
65. E. Zitzler, K. Deb, and L. Thiele, "Comparison of multiobjective evolutionary algorithms: Empirical results," Evol. Comput., vol. 8, no. 2, pp. 173-195, 2000.