MOEA/D with Baldwinian learning inspired by the regularity property of continuous multiobjective problem

Neurocomputing

Volumn 145, Issue , 2014, Pages 336-352

  Ma, Xiaoliang ^a   Liu, Fang ^a   Qi, Yutao ^a   Li, Lingling ^a   Jiao, Licheng ^a   Liu, Meiyun ^a   Wu, Jianshe ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Memetic strategy; MOEA D; Multiobjective optimization; Regularity property

Indexed keywords

EVOLUTIONARY ALGORITHMS; PARETO PRINCIPLE;

CONVERGENCE OF SOLUTIONS; MEMETIC; MOEA/D; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE PROBLEM; MULTIOBJECTIVE OPTIMIZATION PROBLEMS (MOPS); REGULARITY PROPERTIES; SINGLE OBJECTIVE OPTIMIZATION;

MULTIOBJECTIVE OPTIMIZATION;

ARTICLE; BALDWINIAN LEARNING OPERATOR; CHILD PARENT RELATION; CLINICAL EFFECTIVENESS; CONTINUOUS MULTIOBJECTIVE PROBLEM; EVOLUTION; EVOLUTIONARY ALGORITHM; HUMAN; INTERMETHOD COMPARISON; MATHEMATICAL PHENOMENA; MULTI OBJECTIVE EVOLUTIONARY ALGORITHM BASED DECOMPOSITION; NNIA ALGORITHM; NSGA II ALGORITHM; OMOPSO ALGORITHM; PRIORITY JOURNAL; PROBABILITY; PROBLEM SOLVING; PROGENY; SEARCH ENGINE;

EID: 84906818172     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.05.025     Document Type: Article

References (40)

1. E. Zitzler, M. Laumanns, L. Thiele, Spea2: improving the strength Pareto evolutionary algorithm for multiobjective optimization, in: Proceedings of Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, 2001, pp. 95-100.
2. Deb K., Agrawal S., Pratap A., Meyarivan T. A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Trans. Evol. Comput. 2002, 6(2):182-197.
3. E. Zitzler, S. Kunzli, Indicator-based selection in multiobjective search, in: Parallel Problem Solving in Nature, Lecture Notes on Computer Science, 2004, pp. 832-842.
4. Zhang Q., Li H. MOEA/D. a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 2007, 11(6):712-731.
5. Zhao S., Suganthan P., Zhang Q. MOEA/D with an ensemble of neighbourhood sizes. IEEE Trans. Evol. Comput. 2012, 16(3):442-446.
6. M. Nasir, A. Mondal, S. Sengupta, S. Das, A. Abraham, An improved multiobjective evolutionary algorithm based on decomposition with fuzzy dominance, in: Congress on Evolutionary Computation IEEE, 2011, pp. 765-772.
7. Sengupta S., Das S., Nasir M., Vasilakos A., Pedrycz W. An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Trans. Syst. Man Cybern. Part C 2012, 42(6):1093-1102.
8. Miettinen K. Nonlinear Multiobjective Optimization 1999, Kluwer Academic Publishers, Norwell, America.
9. Hillermeier C. Nonlinear Multiobjective Optimization-A Generalized Homotopy Approach 2001, Birkhauser, Basel, Switzerland.
10. Zhang Q., Zhou A., Jin Y. A regularity model-based multiobjective estimation of distribution algorithm. Rm-meda. IEEE Trans. Evol. Comput. 2008, 12(1):41-63.
11. Zhou A., Zhang Q., Jin Y. Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm. IEEE Trans. Evol. Comput. 2009, 13(5):1167-1189.
12. Baldwin J. A new factor in evolution. Am. Nat. 1896, 30:441-451.
13. Hinton G., Nowlan S. How learning can guide evolution. Complex Syst. 1987, 1:495-502.
14. Gong M., Jiao L., Zhang L. Baldwinian learning in clonal selection algorithm for optimization. Inf. Sci. 2010, 180:1218-1236.
15. Qi Y., Liu F., Liu M., Gong M., Jiao L. Multi-objective immune algorithm with Baldwinian learning. Appl. Soft Comput. 2012, 12:2654-2674.
16. Gong M., Jiao L., Du H., Bo L. Multi-objective immune algorithm with nondominated neighbor-based selection: NNIA. Evol. Comput. 2008, 16:225-255.
17. Yang D., Jiao L., Gong M., Feng J. Adaptive ranks and k-nearest neighbor list based multiobjective immune algorithm. Comput. Intell. 2010, 26:359-385.
18. Li H., Zhang Q. Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 2009, 12(2):284-302.
19. H. Ishibuchi, Y. Sakane, N. Tsukamoto, Y. Nojima, Simultaneous use of different scalarizing functions in MOEA/D, in: Genetic and Evolutionary Computation Conference, 2010, pp. 519-526.
20. Mei Y., Tang K., Yao X. Decomposition-based memetic algorithm for multi-objective capacitated arc routing problem. IEEE Trans. Evol. Comput. 2011, 15(2):151-165.
21. K. Deb, H. Jain, An Improved NSGA-II Procedure for Many-objective Optimization, Part I: Solving Problems with Box Constraints, Technical Report 2012009, KanGAL, 2012.
22. I. Giagkiozis, R. Purshouse, P. Fleming, Generalized Decomposition and Cross Entropy Methods for Many-objective Optimization, Technical Report 1029, ACSE Research, 2012.
23. Lara A., Sanchez G., Coello Coello C., Schutze O. A new local search strategy for memetic multiobjective evolutionary algorithms. HCS. IEEE Trans. Evol. Comput. 2010, 14(1):112-132.
24. Y. Jin, B. Sendhoff, Connectedness, regularity and the success of local search in evolutionary multi-objective optimization, in: Congress on Evolutionary Computation, 2003, pp. 1910-1917.
25. Kambhatla N., Leen T. Dimension reduction by local principal component analysis. Neural Comput. 1997, 9(7):1493-1516.
26. W. Hart, N. Krasnogor, J. Smith, Recent Advances in Memetic Algorithms, Springer, 2004, Ch. Memetic Evolutionary Algorithms, pp. 3-30.
27. Deb K., Beyer H. Self-adaptive genetic algorithms with simulated binary crossover. Evol. Comput. 2001, 9(2):197-221.
28. Fang K., Wang Y. Number-theoretic Methods in Statistics 1994, Chapman and Hall, London, England.
29. Duda R., Hart P., Stork D. Pattern Classification 2000, Wiley-Interscience, New York, America. 2nd ed.
30. M. Sierra, C. Coello, Improving PSO-based multi-objective optimization using crowding, mutation and ε-dominance, in: International Conference on Evolutionary Multi-Criterion Optimization, EMO 2005, 2005, pp. 505-519.
31. Zitzler E., Deb K., Thiele L. Comparison of multiobjective evolutionary algorithms. empirical results. Evol. Comput. 2000, 8(2):173-195.
32. K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable multi-objective optimization test problems, in: Proceedings of Congress on Evolutionary Computation, 2002, pp. 825-830.
33. Q. Zhang, A. Zhou, S. Zhao, P. Suganthan, W. Liu, S. Tiwari, Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition, Technical Report CES-887, 2008.
34. Zitzler E., Thiele L., Laumanns M., Fonseca C., Fonseca V. Performance assessment of multiobjective optimizers. an analysis and review. IEEE Trans. Evol. Comput. 2003, 7(2):117-132.
35. Derrac J., Garca S., Molina D., Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 2011, 1(1):3-18.
36. Mirjalili S., Lewis A. S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 2013, 685-692.
37. K. Deb, A. Pratap, T. Meyarivan, Constrained test problems for multi-objective evolutionary optimization, in: Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, 2001, pp. 284-298.
38. Fonseca C., Fleming P. Multiobjective optimization and multiple constraint handling with evolutionary part i. a unified formulation. IEEE Trans. Syst. Man Cybern. Part A 1998, 28(1):26-37.
39. Hastie T., Stuetzle W. Principal curves. J. Am. Stat. Assoc. 1989, 84(406):502-516.
40. Jan M.A., Zhang Q.F. MOEA/D for constrained multiobjective optimization: some preliminary experimental results, in:. UKCI 2010.