BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems

Computers and Operations Research

Volumn 40, Issue 1, 2013, Pages 282-302

  Li, Xiang ^a   Du, Gang ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Atrie tree; Binary search method; Boundary simulation method; Constrained multi objective optimization; Constraint handling; Genetic algorithms; Inequality constraint; Multi objective optimization; Pareto front; Pareto optimum; Pareto set; Population diversity; Rtrie tree; Trie tree

Indexed keywords

ATRIE-TREE; BINARY SEARCH; CONSTRAINED MULTI-OBJECTIVE OPTIMIZATIONS; CONSTRAINT HANDLING; INEQUALITY CONSTRAINT; MULTI OBJECTIVE OPTIMIZATIONS (MOO); PARETO FRONT; PARETO OPTIMA; PARETO SET; POPULATION DIVERSITY; RTRIE-TREE; SIMULATION METHODS; TRIE-TREE;

BINARY TREES; CONSTRAINT THEORY; FORESTRY; MULTIOBJECTIVE OPTIMIZATION; NUMERICAL METHODS;

GENETIC ALGORITHMS;

ALGORITHMS; FORESTRY; OPTIMIZATION; SET; SIMULATION;

EID: 84866175624     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2012.07.014     Document Type: Article

References (69)

1. D.E. Goldberg Genetic algorithms in search, optimization, and machine learning 1989 Addison Wesley
2. Schaffer JD. Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the first international conference on genetic algorithms, 1985. p. 93-100.
3. N. Srinivas, and K. Deb Multiobjective optimization using nondominated sorting in genetic algorithms Evolutionary Computation 2 3 1994 221 248
4. Deb K, Agrawal S, Paratap A, Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the parallel problem solving from nature VI conference, 2000. p. 849-58.
5. E. Zitzler, and L. Thiele Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach IEEE Transactions on Evolutionary Computation 3 4 1999 257 271
6. E. Zitzler, M. Laumanns, and L. Thiele SPEA2: improving the strength Pareto evolutionary algorithm 2001 Swiss Federal Institute Technology
7. Corne D, Knowles J, Oates M. The pareto envelope-based selection algorithm for multiobjective optimization. In: Proceedings of the parallel problem solving from nature VI conference, 2000. p. 839-48.
8. Corne D, Jerram N, Knowles J, Oates M. PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the genetic and evolutionary computation conference, 2001. p. 283-90.
9. Knowles J, Corne D. The Pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimization. In: Proceedings of the third Australia-Japan joint workshop on intelligent and evolutionary systems, 1999. p. 209-16.
10. J. Knowles, and D. Corne Approximating the nondominated front using the Pareto archived evolution strategy Evolutionary Computation 8 2 2000 149 172
11. J. Hajek, A. Szollos, and J. Sistek A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization Advances in Engineering Software 41 7 2010 1031 1057
12. L.V. Santana-Quintero, and C.A.C. Coello An algorithm based on differential evolution for multi-objective problems International Journal of Computation Intelligence Research 1 2 2005 151 169
13. Hertnandez-Diaz AG, Santana-Quintero LV, Coello CAC. A new proposal for multi-objective optimization using differential evolution and rough sets theory. In: Proceedings of the genetic and evolutionary computation conference, 2006. p. 675-82.
14. L.V. Santana-Quintero, A.G. Hertnandez-Diaz, and C.A.C. Coello DEMORS: a hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems Computers & Operations Research 37 3 2009 470 480
15. W.Y. Gong, and Z.H. Cai An improved multiobjective differential evolution based on Pareto-adaptive ε-dominance and orthogonal design European Journal of Operational Research 198 2 2009 576 601
16. O.B. Augusto, S. Rabeau, P.H. Depince, and F. Bennis Multi-objective genetic algorithms: a way to improve the convergence rate Engineering Application of Artificial Intelligence 19 5 2006 501 510
17. K.C. Tan, C.K. Goh, Y.J. Yang, and T.H. Lee Evolving better population distribution and exploration in evolutionary multi-objective optimization European Journal of Operational Research 171 2 2006 463 495
18. C.M. Fonseca, and P.J. Fleming An overview of evolutionary algorithms in multiobjective optimization Evolutionary Computation 3 1 1995 1 16
19. A. David, V. Veldhuizen, and G.B. Lamont Multiobjective evolutionary algorithms: analyzing the state-of-the-art Evolutionary Computation 8 2 2000 125 147
20. Zitzler E, Laumanns M, Bleuler S. A tutorial on evolutionary multiobjective optimization. In: Metaheuristics for multiobjective optimisation. Springer-Verlag; 2003. p. 3-38.
21. A. Konak, D.W. Coit, and A.E. Smith Multi-objective optimization using genetic algorithm: a tutorial Reliability Engineering and System Safety 91 9 2006 992 1007 (Special Issue - Genetic Algorithms and Reliability)
22. Coello CAC. Evolutionary multi-objective optimization: current state and future challenges. In: Proceedings on fifth international conference on hybrid intelligent systems, 2006. p. 1-15.
23. Mezura-Montes E, Reyes-Sierra M, Coello CAC. Multi-objective optimization using differential evolution: a survey of the state-of-the-art. In: Advanced in differential evolution, 2008. p. 173-96.
24. D.F. Jones, S.K. Mirrazavi, and M. Tamiz Multi-objective meta-heuristics: an overview of the current state-of-the-art European Journal of Operational Research 137 1 2002 1 9
25. Michalewicz Z. Genetic algorithms, numerical optimization, and constraints. In: Proceedings of the sixth international conference on genetic algorithms, 1995. p. 151-8.
26. Michalewicz Z. A survey of constrained handling techniques in evolutionary computation methods. In: Proceedings of the fourth annual conference on evolutionary programming, 1995. p. 135-55.
27. Z. Michalewicz Evolutionary algorithm for constrained parameter optimization problem Evolutionary Computation 4 1 1996 1 32
28. A.E. Eiben Evolutionary algorithms and constraint satisfaction: definitions, survey, methodology, and research directions Theoretical Aspects of Evolutionary Computing 2001 13 58
29. C.A.C. Coello Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art Computer Methods in Applied Mechanics and Engineering 191 11 2002 1245 1287
30. S. Salcedo-Sanz A survey of repair methods used as constraint handling techniques in evolutionary algorithms Computer Science Review 3 3 2009 175 192
31. Binh TT, Korn U. MOBES: A multiobjective evolution strategy for constrained optimization problems. In: Proceedings of the third international conference on genetic algorithms, 1997. p. 176-82.
32. Sarker R, Abbass HA, Karim S. An evolutionary algorithm for constrained multiobjective optimization problems. In: Proceedings of the fifth Australasia-Japan joint workshop on intelligent and evolutionary system, 2001. p. 113-22.
33. A. Kurpati, S. Azarm, and J. Wu Constraint handling improvements for multiobjective genetic algorithms Structural and Multidisciplinary Optimization 23 3 2002 204 213
34. Y.G. Woldesenbet, G.G. Yen, and B.G. Tessema Constraint handling in multiobjective evolutionary optimization IEEE Transactions on Evolutionary Computation 13 3 2009 514 525
35. Jimenez F, Verdegay JL. Constrained multiobjective optimization by evolutionary algorithms. In: Proceedings of the international ICSC symposium on engineering of intelligent systems, 1998. p. 266-71.
36. Deb K, Pratap A, Meyarivan T. Constrained test problems for multi-objective evolutionary optimization. In: Proceedings of the first international conference on evolutionary multi-criterion optimization, 2000. p. 284-98.
37. Jimenez F, Gomez-Skarmeta AF, Sanchez G. An evolutionary algorithm for constrained multiobjective optimization. In: Proceedings of the evolutionary computation conference, 2002. p. 1133-8.
38. S.Y. Zeng, L.S. Kang, and L.X. Ding An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints Evolutionary Computation 12 1 2004 77 98
39. Oyama A, Shimoyama K, Kozo F. New constraint-handling method for multi-objective multi-constraint evolutionary optimization and its application to space plane design. In: Proceedings of the evolutionary and deterministic methods for design, optimization and control with applications to industrial and societal problems conference, 2005. p. 416-28.
40. Young N. Blended ranking to cross infeasible regions in constrained multiobjective problems. In: Proceedings of the international conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce, 2005. p. 191-6.
41. C.M. Fonseca, and P.J. Fleming Multiobjective optimization and multiple constraint handling with evolutionary algorithms: Part I: a unified formulation IEEE Transactions on Systems and Cybernetics - Part A: Systems and Humans 28 1 1998 26 37
42. Harada K, Sakuma J, Ono Isao, Kobayashi S. Constraint-handling method for multi-objective function optimization: pareto descent repair operator. In: Proceedings of the fourth international conference on evolutionary multi-criterion optimization, 2007. p. 156-70.
43. Fonseca CM, Fleming OJ. Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceeding of the fifth international conference on genetic algorithms, 1993. p. 416-23.
44. M.T. Jensen Reducing the run-time complexity of multiobjective EAs: the NSGA-II and other algorithms IEEE Transactions on Evolutionary Computation 7 5 2003 503 515
45. I. Alberto, and P.M. Mateo Representation and management of MOEA populations based on graphs European Journal of Operational Research 159 16 2004 52 65
46. C. Shi, Z.Y. Yan, K.V. Lu, Z.Z. Shi, and B. Wang A dominance tree and its application in evolutionary multi-objective optimization Information Sciences 179 20 2009 3540 3560
47. C. Shi, Z.Y. Yan, Z.Z. Shi, and L. Zhang A fast multiobjective evolutionary algorithm based on a tree structure Applied Soft Computing 10 2 2010 469 480
48. Laumanns M, Zitzler E, Thiele L. A unified model for multi-objective evolutionary algorithms with elitism. In: Proceedings of the congress on evolutionary computation, 2000. p. 46-53.
49. Everson RM, Fieldsend JE, Singh S. Full elite sets for multi-objective optimisation. In: Proceedings of the fifth international conference on adaptive computing in design and manufacture, 2002.
50. J.E. Fieldsend, R.M. Everson, and S. Singh Using unconstrained elite archives for multi-objective optimisation IEEE Transactions on Evolutionary Computation 7 3 2001 305 323
51. Mostaghim S, Teich J. Quad-trees: a data structure for storing pareto-sets in multi-objective evolutionary algorithms with elitism. In: Evolutionary multiobjective optimization advanced information and knowledge processing, 2005. p. 81-104.
52. H.M. Lu, and G.G. Yen Rank-density based multiobjective genetic algorithm and benchmark test function study IEEE Transactions on Evolutionary Computation 7 4 2003 343 352
53. G.G. Yen, and H.M. Lu Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation IEEE Transactions on Evolutionary Computation 7 3 2003 253 274
54. Tanaka M. GA-based decision support system for multi-criteria optimization. In: Proceedings of the international conference of systems, 1995. p. 1556-61.
55. Osyczka A, Kundu S. A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm. 1995;10(2):94-9.
56. K. Deb Multi-objective genetic algorithm: problem difficulties and construction of test problems Evolutionary Computation 7 3 1999 205 230
57. Deb K, Thiele L, Laumanns M, Zitzler E. Scalable test problems for evolutionary multiobjective optimization. In: Proceedings of the evolutionary multiobjective optimization conference, 2005. p. 105-45.
58. Deb K, Sinha A, Kukkonen S. Multi-objective test problems, linkages, and evolutionary methodologies. In: Proceedings of the conference of genetic and evolutionary computation, 2006. p. 1141-8.
59. Huband S, Barone L, While L, Hingston P. A scalable multi-objective test problem toolkit. In: Proceedings of the evolutionary multi-criterion optimization: third international conference, 2005. p. 280-94.
60. H. Li, and Q. Zhang Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II IEEE Transaction on Evolutionary Computation 13 2 2009 284 302
61. Okabe T, Jin Y, Olhofer M, Sendhoff B. On test functions for multi-objective optimization. In: Proceedings of the parallel problem solving from nature conference, 2004. p. 792-802.
62. S. Huband, P. Hingston, L. Barone, and L. While A review of multiobjective test problems and a scalable test problem toolkit Evolutionary Computation 10 5 2006 477 506
63. E. Zitzler, K. Deb, and L. Thiele Comparison of multiobjective evolutionary algorithms: empirical results Evolutionary Computation 8 2 2000 173 195
64. Knowles J, Corne D. On metrics for comparing non-dominated sets. In: Proceedings of the congress on evolutionary computation, 2002. p. 711-6.
65. Okabe T, Jin Y, Sendhoff B. A critical survey of performance indices for multi-objective optimisation. In: Proceedings of the congress on evolutionary computation, 2003. p. 878-85.
66. E. Zitzler, and L. Thiele Performance assessment of multiobjective optimizers: an analysis and review IEEE Transactions on Evolutionary Computation 7 2 2003 117 132
67. E. Fredkin Trie memory Communications of the ACM 3 9 1960 490 499
68. C.A.C. Coello, G.T. Pulido, and M.S. Lechuga Handling multiple objectives with particle swarm optimization IEEE Transactions on Evolutionary Computation 8 3 2004 256 279
69. X. Li, and G. Du Inequality constraint handling in genetic algorithms using a boundary simulation method Computes & Operations Research 39 3 2012 521 540