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An integrated method of multi-objective optimization for complex mechanical structure. (English) Zbl 1423.74736

Summary: An integrated method of multi-objective optimization for complex mechanical structures is presented, which integrates prototype modeling, FEM analysis and optimization. To explore its advantages over traditional methods, optimization of a manipulator in hybrid mode aerial working vehicle (HMAWV) is adopted. The objective is to increase its working domain and decrease the cost under the constraint of enough strength, and the design variables are geometric dimensions. NLPQL and NSGA-II are synthesized to achieve optimal solutions. The results indicated that this integrated method was more efficient than enumerative search algorithm. NSGA-II could approximate the global Pareto front precisely, and the relative error between NLPQL and NSGA-II is trivial. Therefore, this integrated method is effective and shows a potential in engineering applications.

MSC:

74P10 Optimization of other properties in solid mechanics
74S05 Finite element methods applied to problems in solid mechanics

Software:

NLPQL; NSGA-II; ANSYS
Full Text: DOI

References:

[1] Wang JT, Johnson TF, Sleight DW, Saether E. Cryogenic tank structure sizing with structural optimization method. A01-25431 AIAA 2001-1599.
[2] Leiva JP, Watson BC, Kosaka Iku. Modern structural optimization concepts applied to topology optimization. In: Structural dynamics and materials conference, vol. 3; 1999. p. 1589 – 96.
[3] Biancolini ME, Brutti C, Pezzuti E. Shape optimization for structural design by means of finite elements method. In: XII ADM international conference – Grand Hotel – Rimini – Italy, 5th – 7th September 2001.
[4] Barker DK, Johnson JC, Johnson EH, Layfield DP. Integration of external design criteria with MSC/Nastran structural analysis and optimization. In: MSC 3rd worldwide aerospace users conference and technology; 2002.
[5] Hansen, L. U.; Horst, P.: Multilevel optimization in aircraft structural design evaluation, Comput struct 86, 104-118 (2008)
[6] Bakhtiary N, Allinger P, Friedrich M, Mulfinger F, Sauter J, Muller O et al. A new approach for sizing, shape and topology optimization. In: SAE international congress and exposition 1996, Detroit/Michigan (USA), 26 – 29 February 1996.
[7] Meske R, Sauter J, Güngör Z. Recent improvements in topology and shape optimization and the integration into the virtual product development process. In: NAFEMS World congress; 2001.
[8] Ilzhöfer B, Müller O, Häußler P, Emmrich D, Allinger P. Shape optimization based on parameters from lifetime prediction. In: NAFEMS seminar fatigue analysis, 8 – 9 November 2000.
[9] Giger, M.; Ermanni, P.: Development of CFRP racing motorcycle rims using a heuristic evolutionary algorithm approach, Struct multidisc optim 30, 54-65 (2005)
[10] Hilmann, J.; Paas, M.; Haenschke, A.; Vietor, T.: Automatic concept model generation for optimization and robust design of passenger cars, Adv eng softw 38, 795-801 (2007)
[11] Qian C, Yuan C. An integrated process of CFD analysis and design optimization with underhood thermal application. Society of Automotive Engineers, 2001-01-0637.
[12] Cullimore B, Panczak T, Baumann J, Genberg V, Kahan M. Automated multidisciplinary optimization of a space-based telescope. SAE 2002-01-2445.
[13] Release 10.0 document for ANSYS. ANSYS. Inc.
[14] Deb, K.: An introduction to genetic algorithms, Sadhana 24, No. 4 – 5, 293-315 (1999) · Zbl 1075.90565
[15] Knowles J, Corne D. The Pareto archived evolution strategy: a new baseline algorithm for multi-objective optimization. In: Proceedings of the 1999 congress on evolutionary computation. Piscataway (NJ): IEEE Press; 1999. p. 98 – 105.
[16] Horn J, Nafploitis N, Goldberg DE. A niched Pareto genetic algorithm for multi-objective optimization. In: Z. Michalewicz, editor. Proceedings of the first IEEE conference on evolutionary computation. Piscataway (NJ): IEEE Press; 1994. p. 82 – 7.
[17] Zitzler E. Evolutionary algorithms for multi-objective optimization: methods and applications. Doctoral dissertation ETH 13398. Zurich (Switzerland): Swiss Federal Institute of Technology (ETH); 1999.
[18] Srinivas, N.; Deb, K.: Multi-objective function optimization using nondominated sorting genetic algorithms, Evol comput 2, No. 3, 221-248 (1995)
[19] Goldberg, D. E.: Genetic algorithms in search, optimization and machine learning, (1989) · Zbl 0721.68056
[20] Coello, C. A. C.; De Computacion, S.; Zacatenco, C. S. P.: Twenty years of evolutionary multi-objective optimization: a historical view of the field, CSP zacatenco-IEEE comput intell mag 1, No. 1, 28-36 (2006)
[21] Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE trans evol comput 6, No. 2, 182-197 (2002)
[22] Deb K, Rao UB, Karthik S. Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Evolutionary multi-criterion optimization – 4th international conference, EMO 2007, vol. 4403 LNCS. p. 803 – 17.
[23] Nandasana, A. D.; Ray, A. K.; Gupta, S. K.: Applications of the non-dominated sorting genetic algorithm (NSGA) in chemical reaction engineering, Int J chem react eng 1 (2003)
[24] Hiroyasu T, Nakayama S, Miki M. Comparison study of SPEA2+, SPEA2, and NSGA-II in diesel engine emissions and fuel economy problem. In: The 2005 IEEE congress on evolutionary computation (IEEE Cat. No. 05TH8834), vol. 1; 2005. p. 236 – 42
[25] Kitagawa, H.: Activities in experimental stress and strain analysis in some asian countries, J exp mech 21, No. 5, 173-176 (1981)
[26] Laermann, K. H.: Recent developments and further aspects of experimental stress analysis in the federal republic of Germany and western Europe, J exp mech 21, No. 2, 49-58 (1981)
[27] Ch”e, E. U.; Simakov, S. R.: Method of temperature error compensation in strain transducers, J meas tech 36, No. 7, 781-783 (1993)
[28] Saitou, K.; Izui, K.; Nishiwaki, S.; Papalambros, P.: A survey of structural optimization in mechanical product development, J comput inform sci eng 5, No. 3, 214-226 (2005)
[29] Schittkowski, K.: NLPQL: a Fortran subroutine solving constrained nonlinear programming problems, Ann oper res 5, No. 1 – 4, 485-500 (1986)
[30] Schittkowski, K.; Zillober, C.: Nonlinear programming: algorithms, software, and applications – from small to very large scale optimization, Syst model optim 166, 73-107 (2005) · Zbl 1151.90554
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