Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms

Energy Conversion and Management

Volumn 48, Issue 3, 2007, Pages 1029-1041

  Atashkari, K ^a   Nariman Zadeh, N ^a   Golcu M ^b   Khalkhali, A ^a   Jamali, A ^a  

^a UNIVERSITY OF GUILAN   (Iran)

^b PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

GA; GMDH; Multi objective optimization; Spark ignition engine; Variable valve timing

Indexed keywords

DATA HANDLING; EVOLUTIONARY ALGORITHMS; FUEL CONSUMPTION; NEURAL NETWORKS; OPTIMIZATION; PARETO PRINCIPLE; POLYNOMIALS; TORQUE;

MULTI-OBJECTIVE OPTIMIZATION; PARETO OPTIMIZATION; VARIABLE VALVE-TIMING;

INTERNAL COMBUSTION ENGINES;

EID: 33846328384     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2006.07.007     Document Type: Article

References (49)

1. Dresner T, Barkan P. A review and classification of variable valve-timing mechanisms. SAE paper, 890674, 1989.
2. Maekawa K, Ohsawa N. Development of a valve-timing control system. SAE paper, 890674, 1989.
3. Asmus TG. Perspectives on application of variable valve-timing. SAE paper, 910445, 1991.
4. Nakayasu T, Yamada H, Suda T, Iwase N, Takahashi K. Intake and exhaust systems equipped with a variable valve control device for enhancing of engine power. SAE paper, 01-0247, 2001.
5. Sher E., and Bar-Kohany T. Optimization of variable valve-timing for maximizing performance of an unthrottled SI engine: a theoretical study. Energy 27 8 (2002) 758-775
6. Kohany T, Sher E. Using the second law of thermodynamics to optimize variable valve-timing for maximizing torque in a throttled SI engine. SAE paper, 1999-01-0328, 1999.
7. Ma TH. Effect of variable engine valve-timing on fuel economy. SAE paper, 880390, 1988.
8. Astrom KJ, Eykhoff P. System identification, a survey. Automatica 7-123-62, 1971.
9. Sanchez E., Shibata T., and Zadeh L.A. Genetic algorithms and fuzzy logic systems (1997), World Scientific, Riveredge, NJ
10. Kristinson K., and Dumont G. System identification and control using genetic algorithms. IEEE Trans Syst Man Cybern 22 5 (1992) 1033-1046
11. Koza J. Genetic programming, on the programming of computers by means of natural selection (1992), MIT Press, Cambridge, MA
12. Iba H, Kuita T, deGaris H, Sator T. System identification using structured genetic algorithms. In: Proceedings of the 5th international conference on genetic algorithms, ICGA'93, USA, 1993.
13. Rodríguez-Vázquez K. Multiobjective evolutionary algorithms in non-linear system identification. Ph.D. Thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK, 1999.
14. Fonseca C.M., and Fleming P.J. Nonlinear system identification with multiobjective genetic algorithms. Proceedings of the 13th World congress of the international federation of automatic control (1996), Pergamon Press, San Francisco, CA 187-192
15. Liu G.P., and Kadirkamanathan V. Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms. IEE Proc Control Theory Appl 146 5 (1999) 373-382
16. Ivakhnenko A.G. Polynomial theory of complex systems. IEEE Trans Syst Man Cybern, SMC-1 (1971) 364-378
17. Farlow S.J. Self-organizing method in modelling: GMDH type algorithm (1984), Marcel Dekker Inc.
18. Mueller J.-A., and Lemke F. Self-organising data mining: an intelligent approach to extract knowledge from data (2000), Pub. Libri, Hamburg
19. Iba H., deGaris H., and Sato T. A numerical approach to genetic programming for system identification. Evol Comput 3 4 (1996) 417-452
20. Nariman-Zadeh N., Darvizeh A., and Ahmad-Zadeh G.R. Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc I MECH E Part B: J Eng Manuf 217 (2003) 779-790
21. Nariman-Zadeh N., Darvizeh A., Felezi M.E., and Gharababaei H. Polynomial modelling of explosive compaction process of metallic powders using GMDH-type neural networks and singular value decomposition. Modell Simul Mater Sci Eng 10 6 (2002) 727-744
22. Nariman-zadeh N., Darvizeh A., Darvizeh M., and Gharababaei H. Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition. J Mater Process Technol 128 1 (2002) 80-87
23. Porto V.W. Evolutionary computation approaches to solving problems in neural computation. In: Back T., Fogel D.B., and Michalewicz Z. (Eds). Handbook of evolutionary computation (1997), Institute of Physics Publishing and Oxford University Press, New York D1.2:1-D1.2:6
24. Yao X. Evolving artificial neural networks. Proc IEEE 87 9 (1999) 1423-1447
25. Vasechkina E.F., and Yarin V.D. Evolving polynomial neural network by means of genetic algorithm: some application examples. Complex Int 9 (2001)
26. Arora J.S. Introduction to optimum design (1989), McGraw-Hill
27. Rao S.S. Engineering optimization: theory and practice (1996), John Wiley & Sons
28. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning (1989), Addison-Wesley, New York
29. Back T., Fogel D.B., and Michalewicz Z. Handbook of evolutionary computation (1997), Institute of Physics Publishing and Oxford University Press, New York
30. Renner G., and Ekart A. Genetic algorithms in computer aided design. Comput Aided Des 35 (2003) 709-726
31. Srinivas N., and Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2 3 (1994) 221-248
32. Fonseca C.M., and Fleming P.J. Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. In: Forrest S. (Ed). Proceedings of the fifth international conference on genetic algorithms (1993), Morgan Kaufmann, San Mateo, CA 416-423
33. Coello Coello C.A., and Christiansen A.D. Multiobjective optimization of trusses using genetic algorithms. Comput Struct 75 (2000) 647-660
34. Coello Coello C.A., Van Veldhuizen D.A., and Lamont G.B. Evolutionary algorithms for solving multi-objective problems (2002), Kluwer Academic Publishers, NY
35. Pareto V. Cours d'economic ploitique (1896), Lausanne, Switzerland, Rouge
36. Rosenberg RS. Simulation of genetic populations with biochemical properties. Ph.D. Thesis, University of Michigan, Ann Harbor, Michigan, 1967.
37. Schaffer JD. Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette JJ, editor. Proceedings of the first international conference on genetic algorithms and their applications, London, Lawrence Erlbaum, 1985. p. 93-100.
38. Zitzler E, Thiele L. An evolutionary algorithm for multiobjective optimization: the strength Pareto approach. Tech. Report 43, Computer Engineering and Communication Network Lab, Swiss federal Ins. of Tech., Zurich, 1998.
39. Knowles J., and Corne D. The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization. Proceedings of the 1999 congress on evolutionary computation (1999), IEEE Service Center, Piscataway, NJ 98-105
40. Horn J., Nafpliotis N., and Goldberg D.E. A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the first IEEE conference on evolutionary computation, IEEE World congress on computational intelligence vol. 1 (1994), IEEE Service Centre, Piscataway, NJ 82-87
41. Coello Coello C.A. A comprehensive survey of evolutionary based multiobjective optimization techniques. Knowl Inform Syst: An Int J 3 (1999) 269-308
42. Deb K. Multi-objective optimization using evolutionary algorithms (2001), John Wiley, UK
43. Khare V, Yao X, Deb K. Performance scaling of multi-objective evolutionary algorithms. In: Proceedings of the second international conference on evolutionary multi-criterion optimization, (EMO'03), Portugal, 2003.
44. Toffolo A., and Benini E. Genetic diversity as an objective in multi-objective evolutionary algorithms. Evol Comput 11 2 (2003) 151-167
45. Deb K., Agrawal S., Pratap A., and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6 2 (2002) 182-197
46. Coello Coello CA, Becerra RL. Evolutionary multiobjective optimization using a cultural algorithm. In: IEEE Swarm Intelligence Symp, USA, 2003, p. 6-13.
47. Sarker R., Liang K.-H., and Newton C. A new continuous optimization multiobjective evolutionary algorithm. Eur J Oper Res 140 (2002) 12-23
48. Gölcü M., Sekmen Y., Erduranli{dotless} P., and Salman S. Artificial neural-network based modelling of variable valve-timing in a spark-ignition engine. Appl Energy 81 (2005) 187-197
49. Osyezka A. Multicriteria optimization for engineering design. In: Gero J.S. (Ed). Design optimization (1985), Academic Press, NY 193-227