178
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

An improved multi-objective optimization method based on adaptive mutation particle swarm optimization and fuzzy statistics algorithm

&
Pages 2480-2493 | Received 30 Nov 2016, Accepted 14 Feb 2017, Published online: 08 Mar 2017

References

  • Simpson TW, D’souza BS. Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm. Concurr Eng. 2004;12(2):119–129. doi: 10.1177/1063293X04044383
  • Dai Z, Scott MJ. Effective product family design using preference aggregation. J Mech Des. 2006;128(4):659–667. doi: 10.1115/1.2197835
  • Gonzalez-Zugasti JP, Otto KN, Baker JD. Assessing value in platformed product family design. Res Eng Des. 2001;13(1):30–41. doi: 10.1007/s001630100001
  • Thevenot HJ, Nanda J, Simpson TW. A methodology to support product family redesign using genetic algorithm and commonality indices. In: ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (American Society of Mechanical Engineers); 2005. p. 1009–1018.
  • Nayak RU, Chen W, Simpson TW. A variation-based method for product family design. Eng Optim. 2002;34(1):65–81. doi: 10.1080/03052150210910
  • Hernandez G, Allen JK, Woodruff GW, et al. Robust design of families of products with production modeling and evaluation. J Mech Des. 2001;123(2):183–190. doi: 10.1115/1.1359786
  • Goldberg DE, Holland JH. Genetic algorithms and machine learning. Mach Learn. 1988;3(2):95–99. doi: 10.1023/A:1022602019183
  • Xie XF, Zhang WJ, Yang ZL. A dissipative particle swarm optimization. Eprint Arxiv Cs, abs/cs/0505065(8); 2002. p. 1456–1461.
  • Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8(3):240–255. doi: 10.1109/TEVC.2004.826071
  • Pant M, Thangaraj R, Abraham A. Particle swarm optimization using adaptive mutation. In: 2008 19th International Workshop on Database and Expert Systems Applications (IEEE); 2008. p. 519–523.
  • Meyer MH, Lehnerd AP. The power of product platforms: building value and cost leadership. Res Technol Manag. 1997;40(6):526–529.
  • Moon SK, McAdams DA. A market-based design strategy for a universal product family. J Mech Des. 2012;134(11):3–4.
  • Rojas Arciniegas AJ, Kim HM. Incorporating security considerations into optimal product architecture and component sharing decision in product family design. Eng Optim. 2012;44(1):55–74. doi: 10.1080/0305215X.2011.561842
  • Simpson TW, Jiao J, Siddique Z, et al. Advances in product family and product platform design. New York: Springer; 2014.
  • Tao F, Bi LN, Zuo Y, et al. A hybrid group leader algorithm for green material selection with energy consideration in product design. CIRP Ann Manuf Technol. 2016;65(1):9–12. doi: 10.1016/j.cirp.2016.04.086
  • Huang GQ, Li L, Schulze L. Genetic algorithm-based optimisation method for product family design with multi-level commonality. J Eng Des. 2008;19(5):401–416. doi: 10.1080/09544820701642063
  • Moon SK, Park KJ, Simpson TW. Platform design variable identification for a product family using multi-objective particle swarm optimization. Res Eng Des. 2014;25(2):95–108. doi: 10.1007/s00163-013-0166-0
  • Yuan JJ. Optimal design for scale-based product family based on multi-objective firefly algorithm. Comput Integr Manuf Syst. 2012;18:1801–1809.
  • Li ZK, Tan JR, Feng YX, et al. Optimization of scale-based product family using multiobjective genetic algorithm. J Zhejiang Univ Eng Sci. 2008;6:024.
  • Cheng J, Duan G, Liu Z, et al. Interval multiobjective optimization of structures based on radial basis function, interval analysis, and NSGA-II. J Zhejiang Univ Sci A. 2014;15(10):774–788. doi: 10.1631/jzus.A1300311
  • Yang C, Gu L, Gui W. Particle swarm optimization algorithm with adaptive mutation. Comput Eng. 2008;16:188–190.
  • Cheng J, Feng Y, Lin Z, et al. Anti-vibration optimization of the key components in a turbo-generator based on heterogeneous axiomatic design. J Clean Prod. 2017;141:1467–1477. doi: 10.1016/j.jclepro.2016.09.217

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.