ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

Neurocomputing

Volumn 127, Issue , 2014, Pages 65-77

  Venske, Sandra M ^a,b   Gonçalves, Richard A ^a   Delgado, Myriam R ^b  

^a Department of Computer Science   (Brazil)

^b UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

Author keywords

Adaptive differential evolution; Adaptive pursuit; Credit assignment; Multiobjective optimization; Probability matching

Indexed keywords

ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHMS; ADAPTIVE DIFFERENTIAL EVOLUTIONS; ADAPTIVE PURSUIT; CONTINUOUS OPTIMIZATION; CREDIT ASSIGNMENT; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; PROBABILITY MATCHING;

EVOLUTIONARY ALGORITHMS; PROBABILITY; ALGORITHMS; OPTIMIZATION;

MULTIOBJECTIVE OPTIMIZATION;

ADAPTIVE DIFFERENTIAL EVOLUTION FOR MULTIOBJECTIVE PROBLEMS; ADAPTIVE PURSUIT; ARTICLE; CREDIT ASSIGNMENT; DATA PROCESSING; EVOLUTIONARY ALGORITHM; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROBABILITY; PROBABILITY MATCHING; STATISTICAL ANALYSIS; CALCULATION; COMPARATIVE STUDY; KRUSKAL WALLIS TEST; MATHEMATICAL ANALYSIS; PROCESS OPTIMIZATION;

EID: 84888430701     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.06.043     Document Type: Article

References (49)

1. Deb K. Multi-Objective Optimization using Evolutionary Algorithms, Wiley-Interscience Series in Systems and Optimization 2001, John Wiley & Sons, Chichester.
2. C.A.C. Coello, An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends, in: IEEE Congress on Evolutionary Computation, 1999, pp. 3-13.
3. Storn R., Price K. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997, 11(4):341-359.
4. Coello Coello C.A., Lamont G.B., Van Veldhuizen D.A. Evolutionary Algorithms for Solving Multi-Objective Problems 2007, Springer. 2nd edition.
5. Price K., Storn R., Lampinen J. Differential Evolution. A Practical Approach to Global Optimization, Natural Computing Series 2005, Springer.
6. Zhang J., Sanderson A.C. JADE. adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 2009, 13:945-958.
7. Das S., Abraham A., Chakraborty U.K., Konar A. Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation 2009, 13(3):526-553.
8. Dorronsoro B., Bouvry P. Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Transactions on Evolutionary Computation 2011, 15(1):67-98.
9. Epitropakis M.G., Tasoulis D.K., Pavlidis N.G., Plagianakos V.P., Vrahatis M.N. Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Transactions on Evolutionary Computation 2011, 15(1):99-119.
10. S. Venske, R.A. Gonçalves, M. Delgado, ADEMO/D: adaptive differential evolution for multiobjective problems, in: Brazilian Conference on Neural Networks 2012, 2012, pp. 226-231.
11. Engelbrecht A.P. Computational Intelligence. An Introduction 2007, Wiley Publishing. 2nd edition.
12. H. Abbass, The self-adaptive Pareto differential evolution algorithm, in: IEEE Congress on Evolutionary Computation, 2002, pp. 831-836.
13. A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: IEEE Congress on Evolutionary Computation, 2005, pp. 1785-1791.
14. J. Brest, V. Zumer, M.S. Maucec, Self-adaptive differential evolution algorithm in constrained real-parameter optimization, in: IEEE Congress on Evolutionary Computation, 2006, pp. 215-222.
15. Brest J., Greiner S., Boskovic B., Mernik M., Zumer V. Self-adapting control parameters in differential evolution. a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 2006, 10(6):646-657.
16. Nobakhti A., Wang H. A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier. Applied Soft Computing 2008, 8(1):350-370.
17. Teo J. Exploring dynamic self-adaptive populations in differential evolution. Soft Computing 2006, 10:673-686.
18. J. Zhang, A. Sanderson, JADE: self-adaptive differential evolution with fast and reliable convergence performance, in: IEEE Congress on Evolutionary Computation, 2007, pp. 2251-2258.
19. Qin A.K., Huang V.L., Suganthan P.N. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 2009, 13:398-417.
20. K. Li, A. Fialho, S. Kwong, Multi-objective differential evolution with adaptive control of parameters and operators, in: International Conference on Learning and Intelligent Optimization, 2011, pp. 473-487.
21. W. Gong, A. Fialho, Z. Cai, Adaptive strategy selection in differential evolution, in: Conference on Genetic and Evolutionary Computation, 2010, pp. 409-416.
22. Gong W., Fialho lvaro, Cai Z., Li H. Adaptive strategy selection in differential evolution for numerical optimization. an empirical study. Information Sciences 2011, 181(24):5364-5386.
23. Mallipeddi R., Suganthan P., Pan Q., Tasgetiren M. Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing 2011, 11(2):1679-1696.
24. Zhang Q., Li H. MOEA/D. a multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 2007, 11(6):712-731.
25. Li H., Zhang Q. Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 2009, 13(2):284-302.
26. Zhang Q., Liu W., Tsang E., Virginas B. Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Transactions on Evolutionary Computation 2010, 14(3):456-474.
27. Goldberg D.E. Probability matching, the magnitude of reinforcement, and classifier system bidding. Machine Learning 1990, 5(4):407-425.
28. D. Thierens, An adaptive pursuit strategy for allocating operator probabilities, in: Conference on Genetic and Evolutionary Computation, 2005, pp. 1539-1546.
29. R. Gamperle, S.D. Muller, P. Koumoutsakos, A parameter study for differential evolution, in: WSEAS International Conference on Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 2002, pp. 293-298.
30. Sindhya K., Ruuska S., Haanp T., Miettinen K. A new hybrid mutation operator for multiobjective optimization with differential evolution. Soft Computing 2011, 15(10):2041-2055.
31. Liao T.W. Two hybrid differential evolution algorithms for engineering design optimization. Applied Soft Computing 2010, 10(4):1188-1199.
32. Lu H.-C., Chang M.-H., Tsai C.-H. Parameter estimation of fuzzy neural network controller based on a modified differential evolution. Neurocomputing 2012, 89(0):178-192.
33. Qasem S.N., Shamsuddin S.M. Memetic elitist Pareto differential evolution algorithm based radial basis function networks for classification problems. Applied Soft Computing 2011, 11(8):5565-5581.
34. Subudhi B., Jena D. Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing 2011, 74(10):1696-1709.
35. D. Thierens, An adaptive pursuit strategy for allocating operator probabilities, in: Conference on Genetic and Evolutionary Computation, 2005, pp. 1539-1546.
36. Goldberg D.E. Probability matching, the magnitude of reinforcement, and classifier system bidding. Machine Learning 1990, 5:407-425.
37. Zhao S.-Z., Suganthan P.N., Zhang Q. Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE Transactions on Evolutionary Computation 2012, 16(3):442-446.
38. W. Huang, H. Li, On the differential evolution schemes in MOEA/D, in: International Conference on Natural Computation, 2010, pp. 2788-2792.
39. Ehrgott M. A discussion of scalarization techniques for multiple objective integer programming. Annals of Operations Research 2006, 147:343-360.
40. Ong Y.S., Keane A.J. Meta-lamarckian learning in memetic algorithms. IEEE Transactions on Evolutionary Computation 2004, 8(2):99-110.
41. A. Fialho, M. Schoenauer, M. Sebag, Analysis of adaptive operator selection techniques on the royal road and long k-path problems, in: Conference on Genetic and Evolutionary Computation, 2009, pp. 779-786.
42. Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, S. Tiwari, Multiobjective optimization test instances for the CEC 2009 special session and competition, Technical Report, University of Essex and Nanyang Technological University, CES-487, 2008.
43. Bleuler S., Laumanns M., Thiele L., Zitzler E. PISA - a platform and programming language independent interface for search algorithms. Evolutionary Multi-Criterion Optimization 2003, 494-508. C.M. Fonseca, P.J. Fleming, E. Zitzler, K. Deb, L. Thiele (Eds.).
44. M.H. Kim, R.I. McKay, N.X. Hoai, K. Kim, Operator self-adaptation in genetic programming, in: Proceedings of the 14th European Conference on Genetic programming, EuroGP'11, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 215-226.
45. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm. NSGA-II. IEEE Transactions on Evolutionary Computation 2002, 6(2):182-197.
46. Kalyanmoy D. Multi-Objective Optimization using Evolutionary Algorithms 2004, Wiley.
47. Khan Mashwani W., Salhi A. A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation. Applied Soft Computing 2012, 12(9):2765-2780.
48. Q. Zhang, W. Liu, H. Li, The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, in: Congress on Evolutionary Computation, 2009, pp. 203-208.
49. K.T. Fang, C. Ma, Orthogonal and Uniform Design, Science Press, 2001 (in Chinese).