Pareto optimization of a five-degree of freedom vehicle vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA)

Engineering Applications of Artificial Intelligence

Volumn 23, Issue 4, 2010, Pages 543-551

  Nariman Zadeh, N ^a,b,c   Salehpour, M ^b   Jamali, A ^b   Haghgoo, E ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b UNIVERSITY OF GUILAN   (Iran)

^c Faculty of Electrical and Computer Engineering   (Iran)

Author keywords

Genetic algorithm; MUGA; Multi objective optimization Diversity; Pareto; Vehicle vibration model

Indexed keywords

DEGREE OF FREEDOM; DIVERSITY PRESERVING MECHANISM; MULTI OBJECTIVE; MULTI-OBJECTIVE OPTIMIZATION DIVERSITY; OBJECTIVE FUNCTIONS; OBJECTIVE OPTIMIZATION; OPTIMAL DESIGN; PARETO OPTIMIZATION; RELATIVE DISPLACEMENT; SPRUNG MASS; VEHICLE VIBRATIONS;

DAMPING; GENETIC ALGORITHMS; PARETO PRINCIPLE; TIRES; VEHICLES; VIBRATION ANALYSIS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 77950519634     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2009.08.008     Document Type: Article

References (29)

1. Alkhatib R., Nakhaie Jazar G., and Golnaraghi M.F. Optimal design of passive linear suspension using genetic algorithm. Journal of Sound and Vibration 275 (2004) 665-691
2. Barak, P., 1991. Magic numbers in design of suspensions for passenger cars. Passenger Car Meeting, Tennesee, SAE Paper 911921, pp. 53-88.
3. Bouazara, M., 1997. Etude et Anaslyse de la Suspension Active et Semi-active des Vehicules Routters. Ph.D. Thesis, Universite Laval, Canada.
4. Bouazara, M., Richard, M.J., 1996. An optimal design method to control the vibrations of suspensions for passenger cars, in: International Mechanical Engineering Congress and Exposition: The Winter Annual Meeting of ASME, Atlanta, DSC 58, pp. 61-68.
5. Bouazara, M., Richard, M.J., 2001. An Optimization Method Designed to Improve 3-D Vehicle Comfort and Road Holding Capability Through the Use of Active and Semi-active Suspensions, European Journal of Mechanics-A/Solids 20 (3), 509-520.
6. Coello Coello, C.A., http://www.lania.mx/∼ccoello/EMOO/
7. Coello Coello C.A. A comprehensive survey of evolutionary based multiobjective optimization techniques, Knowledge and Information Systems. An International Journal 1 3 (1999) 269-308
8. Coello Coello C.A., and Becerra R.L. Evolutionary multiobjective optimization using a cultural algorithm. In: Swarm I. (Ed). Intelligence Symposium, IEEE Service Center, Piscataway (2003) 6-13
9. Coello Coello C.A., and Christiansen A.D. Multiobjective optimization of trusses using genetic algorithms. Computers & Structures 75 (2000) 647-660
10. Coello Coello C.A., Van Veldhuizen D.A., and Lamont G.B. Evolutionary Algorithms for Solving Multi-Objective Problems (2002), Kluwer Academic, Dordrecht
11. Crolla, D.A., 1992. Semi-active Suspension Control for a Full Vehicule Model, SAE Technical Paper Series 911904, pp. 45-51.
12. Deb K., Agrawal S., Pratap A., and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 2 (2002) 182-197
13. Fonseca, C.M., Fleming, P.J., 1993. Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. In: Forrest, S. (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, pp. 416-423.
14. Goldberg D.E. Genetic algorithms in search. Optimization, and Machine Learning (1989), Addison-Wesley, Reading, MA
15. Gündoǧdu Ö. Optimal seat and suspension design for a quarter car with driver model using genetic algorithms. International Journal of Industrial Ergonomics 37 4 (2007) 327-332
16. Griffin M., Parsons K., and Whitham E. Vibration and comfort. IV. Application of experimental results. Ergonomics 25 (1982) 721-739
17. Hrovat, D., 1991, Optimal active suspensions for 3d vehicule models. In: Proceedings of the American Control Conference, vol. 2, Arizona, USA, pp. 1534-1541.
18. Jamali A., Hajiloo A., and Nariman-zadeh N. Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA). Expert Systems with Applications 37 1 (2010) 401-413
19. Khare, V., Yao, X., Deb, K., 2003. Performance scaling of multi-objective evolutionary algorithms. In: Proceedings of Second International Conference on Evolutionary Multi-Criterion Optimization, EMO '03, Portugal.
20. Knowles,.J., Corne, D., 1999. The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE Service Center, Piscataway, NJ, pp. 98-105.
21. Nariman-Zadeh N., Atashkari K., Jamali A., Pilechi A., and Yao X. Inverse modeling of multi-objective thermodynamically optimized turbojet engine using GMDH-type neural networks and evolutionary algorithms. Engineering Optimization 37 26 (2005) 437-462
22. Pareto V. Cours D'economic Politique (1896), Rouge, Lausanne, Switzerland
23. Rakheja, S., 1985. Computer-aided Dynamic Analysis and Optimal Design of Suspension Systems for Off-road Tractors. PhD Thesis. Concordia University, Canada.
24. Rosenberg, R.S., 1967. Simulation of Genetic Populations with Biochemical Properties, PhD Thesis, University of Michigan, Ann Harbor, MI.
25. Schaffer, J.D., 1985. Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette, J.J. (Ed.), Proceedings of the First International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum, London, pp. 93-100.
26. Srinivas N., and Deb K. Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation 2 3 (1994) 221-248
27. Toffolo A., and Benini E. Genetic diversity as an objective in multiobjective evolutionary algorithms. Evolutionary Computation 11 2 (2003) 151-167
28. Zitzler, E., Thiele, L., 1998. An evolutionary algorithm for multiobjective optimization: the strength Pareto approach. Technical Report 43, Computer engineering and communication network Lab, Swiss Federal Institute Of Technology, Zurich.
29. Zitzler E., Deb K., and Thiele L. Comparison of multi objective evolutionary algorithms: empirical results. Evolutionary Computation 8 2 (2000) 173-175