An investigation on noise-induced features in robust evolutionary multi-objective optimization

Expert Systems with Applications

Volumn 37, Issue 8, 2010, Pages 5960-5980

  Goh, C K ^a   Tan, K C ^a   Cheong, C Y ^a   Ong, Y S ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Evolutionary algorithms; Multi objective optimization; Robust solutions; Robust test functions

Indexed keywords

EFFECTIVE ALGORITHMS; EMPIRICAL ANALYSIS; EMPIRICAL STUDIES; EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION ALGORITHMS; OPTIMIZATION PROBLEMS; PARAMETRIC VARIATION; REAL-WORLD APPLICATION; RESEARCH TOPICS; ROBUST SOLUTIONS; ROBUST TEST FUNCTIONS; ROBUST TESTS; SIMULTANEOUS OPTIMIZATION; STOCHASTIC DEMAND; TEST FUNCTIONS; VEHICLE ROUTING PROBLEMS;

EVOLUTIONARY ALGORITHMS; TEST FACILITIES; TESTING;

MULTIOBJECTIVE OPTIMIZATION;

EID: 77951152431     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.008     Document Type: Article

References (54)

1. Abbass H.A., Alam S., and Bender A. Mebra: Multiobjective evolutionary-based risk assessment. IEEE Computational Intelligence Magazine 4 3 (2009) 29-36
2. Beyer, H. -G., & Sendhoff, B. (2006). Evolution strategies for robust optimization. In Proceedings of the 2006 IEEE congress on evolutionary computation.
3. Beyer H.-G., and Sendhoff B. Functions with noise-induced multimodality. IEEE Transactions on Evolutionary Computation 10 5 (2006) 507-526
4. Bosman P., and Thierens D. The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7 2 (2003) 174-188
5. Branke J. Evolutionary optimization in dynamic environments (2002), Kluwer Academic Publishers
6. Branke, J. (2001). Reducing the sampling variance when searching for robust solution. In Proceedings of the 2001 genetic and evolutionary computation conference (pp. 235-424).
7. Branke, J. (1998). Creating robust solutions by means of evolutionary algorithms. In Proceedings of the fifth international conference on parallel problem solving from nature (pp. 119-128).
8. Bui, L. T., Abbass, H. A., & Essam, D. (2005). Fitness inheritance for noisy evolutionary multi-objective optimization. In Proceedings of the 2005 genetic and evolutionary computation conference (Vol. 1, pp. 779-785).
9. Coello Coello C.A., and Aguirre A.H. Design of combinational logic circuits through an evolutionary multiobjective optimization approach. Artificial Intelligence for Engineering, Design, Analysis and Manufacture 16 1 (2002) 39-53
10. Deb, K., & Goldberg, D.E., (1989). An investigation on niche and species formation in genetic function optimization. In Proceedings of third international conference on genetic algorithms (pp. 42-50).
11. Deb, K., & Gupta, H. (2004). Introducing robustness in multiobjective optimization, Kanpur Genetic Algorithms Lab. (KanGAL), Indian Institute of Technology, Kanpur, India, Technical Report 2004016.
12. Deb, K., Padmanabhan, D., Gupta, S., & Kumar Mall, A. (2007). Reliability-based multi-objective optimization using evolutionary algorithms. In Proceedings of the fourth international conference on evolutionary multi-criterion optimization (pp. 66-80).
13. Dror M., and Trudeau P. Stochastic vehicle routing with modified savings algorithm. European Journal of Operational Research 23 2 (1986) 228-235
14. Dror M., Ball M.O., and Golden B.L. Computational comparison of algorithms for inventory routing. Annals of Operations Research 4 (1985) 3-23
15. Farina M., Deb K., and Amato P. Dynamic multiobjective optimization problems: Test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation 8 5 (2004) 425-442
16. Farina M., and Amato P. A fuzzy definition of optimality for many-criteria optimization problems. IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans 34 3 (2004) 315-326
17. Gallagher M., and Yuan B. A general-purpose tunable landscape generator. IEEE Transactions on Evolutionary Computation 10 5 (2006)
18. Gendreau M., Laporte G., and Sguin R. A tabu search heuristic for the vehicle routing problem with stochastic demands and customers. Operations Research 44 3 (1996) 469-477
19. Gendreau M., Laporte G., and Sguin R. An exact algorithm for the vehicle routing problem with stochastic demands and customers. Transportation Science 29 (1995) 143-155
20. Goh C.K., and Tan K.C. An investigation on noisy environments in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 11 3 (2007) 354-381
21. Goh C.K., and Tan K.C. Evolving the tradeoffs between Pareto-optimality and robustness in multi-objective evolutionary algorithms. In: Yang S., Ong Y.S., and Jin Y. (Eds). Evolutionary computation in dynamic and uncertain environments (2007), Springer 457-478
22. Gunawan S., and Azarm S. Multi-objective robust optimization using a sensitivity region concept. Structural and Multidisciplinary Optimization 29 (2005) 50-60
23. Gupta, H., & Deb, K. (2005). Handling constraints in robust multi-objective optimization. In Proceedings of the 2005 IEEE congress on evolutionary computation (pp. 25-32).
24. Homberger J., and Gehring H. Two evolutionary meta-heuristics for the vehicle routing problem with time windows. INFORMS Journal on Computing 37 3 (1999) 297-318
25. Huband S., Hingston P., Barone L., and White L. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10 5 (2006) 477-506
26. Hughes, E. J. (2001). Evolutionary multi-objective ranking with uncertainty and noise. In Proceedings of the first conference on evolutionary multi-criterion optimization (pp. 329-343).
27. Jin Y., and Branke J. Evolutionary optimization in uncertain environments: A survey. IEEE Transactions on Evolutionary Computation 9 3 (2005) 303-317
28. Jin Y., and Sendhoff B. A systems approach to evolutionary multiobjective structural optimization and beyond. IEEE Computational Intelligence Magazine 4 3 (2009) 62-76
29. Jin, Y., & Sendhoff, B. (2003). Tradeoff between performance and robustness: An evolutionary multiobjective approach. In Proceedings of the second conference on evolutionary multi-criterion optimization (pp. 237-251).
30. Knowles J. Closed-loop evolutionary multiobjective optimization. IEEE Computational Intelligence Magazine 4 3 (2009) 77-91
31. Khor E.F., Tan K.C., Lee T.H., and Goh C.K. A study on distribution preservation mechanism in evolutionary multi-objective optimization. Artificial Intelligence Review 23 1 (2005) 31-56
32. Lambert V., Laporte G., and Louveaux F.V. Designing collection routes through bank branches. Computers and Operations Research 20 (1993) 783-791
33. Laporte G., and Louveaux F.V. In: Crainic T.G., and Laporte G. (Eds). Solving stochastic routing problems with the integer L-shaped method. Fleet management and logistics (1998), Kluwer Academic Publishers, Boston 159-167
34. Laporte G., and Louveaux F.V. The integer L-shape method for stochastic integer problems with complete recourse. Operations Research Letters 13 (1993) 133-142
35. Larson R.C. Transportation of sludge to the 106-mile site: An inventory routing algorithm for fleet sizing and logistic system design. Transportation Science 22 (1988) 186-198
36. Laumanns, M., Zitzler, E., & Thiele, L. (2000). A unified model for multi-objective evolutionary algorithms with elitism. In Proceedings of the 2000 congress on evolutionary computation (Vol. 1, pp. 46-53).
37. Li, M., Azarm, S. & Aute, V. (2005). A multi-objective genetic algorithm for robust design optimization. In Proceedings of the 2005 genetic and Evolutionary Computation Conference (pp. 771-778).
38. Lim, D., Ong, Y. -S., Lim, M. -H., & Jin, Y. (in press). Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty. In S. Yang, Y.S. Ong, Y. Jin (Eds.), Evolutionary computation in dynamic and uncertain environments. Springer.
39. Lu H., and Yen G.G. Rank-based multiobjective genetic algorithm and benchmark test function study. IEEE Transactions on Evolutionary Computation 7 4 (2003) 325-343
40. Ong Y.S., Nair P.B., and Lum K.Y. Min-max surrogate assisted evolutionary algorithm for robust aerodynamic design. IEEE Transactions on Evolutionary Computation 10 4 (2006) 392-404
41. Paenke I., Branke J., and Jin Y. Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation 10 4 (2006) 405-420
42. Ray, T. (2002). Constrained robust optimal design using a multiobjective evolutionary algorithm. In Proceedings of the 2002 congress on evolutionary computation (pp. 419-424).
43. Sendhoff, B., Beyer, H. -G., & Olhofer, M. (2002). On noise induced multi-modality in evolutionary algorithms. In L. Wang, K. Tan, T. Furuhashi, K.-H. Kim, F. Sattar (Eds.), Recent advances in simulated evolution and learning (pp. 219-224).
44. Solomon M.M. Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research 35 2 (1987) 254-265
45. Srinivas N., and Deb K. Multiobjective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation 2 3 (1994) 221-248
46. Tan K.C., Cheong C.Y., and Goh C.K. Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation. European Journal of Operational Research 177 (2007) 813-839
47. Tan K.C., Lee T.H., Khor E.F., and Ang D.C. Design and real-time implementation of a multivariable gyro-mirror line-of-sight stabilization platform. Fuzzy Sets and Systems 128 1 (2002) 81-93
48. Teodorovic, D., & Lucic, P. (2000). Intelligent vehicle routing system. In Proceedings of the IEEE international conference on intelligent transportation systems (pp. 482-487).
49. Teodorovic D., and Pavkovic G. The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain. Fuzzy Sets and Systems 82 3 (1996) 307-317
50. Thompson, A. (1996). Evolutionary techniques for fault tolerance. In Proceedings of the UKACC International Conference on Control (pp. 693-698).
51. Tsutsui S., and Ghosh A. Genetic algorithms with a robust solution searching scheme. IEEE Transactions on Evolutionary Computation 1 3 (1997) 201-208
52. Tsutsui, S., & Ghosh, A.. (1999). A comparative study on the effects of adding perturbations to phenotypic parameters in genetic algorithms with a robust solution searching scheme. In Proceedings of the 1999 IEEE international conference on systems, man, and cybernetics (pp. 585-591).
53. Van Veldhuizen D.A., Zydallis J.B., and Lamont G.B. Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7 2 (2003) 144-173
54. Zitzler E., Deb K., and Thiele L. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8 2 (2000) 173-195