Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
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Applications of Micro Genetic Algorithms to Engineering Design Optimization

마이크로 유전알고리듬의 최적설계 응용에 관한 연구

  • Published : 2003.01.01
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Abstract

The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

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References

  1. David E. Goldberg, 1989, Genetic Algorithms, Addison-Wesley Publishing Company, USA, p. 412
  2. Chaiyaratana, N, Zalzala, A. M. S, 1997, 'Recent Developments in Evolutionary and Genetic Algorithms: Theory and Applications,' Genetic Algorithms in Engineering Systems: Innovations and Applications, 2-4 September. NO. 446 pp. 270-277
  3. Krishnakumar, K, 1989, 'Micro-Genetic Algorithms for stationary and non-stationary function optimization,' Intelligent Control and Adaptive Systems, Vol 1196, pp. 289-296
  4. Coello, C. A. and Pulido, G. T, 2001, 'Multiobjective Optimization using a Micro-Genetic Algorithm', GECCO 2001, pp. 274-282
  5. Jeongsu Hwang, 2002, Fuzzy Modeling and Fuzzy Rule Generation in Response Surface Based Approximate Optimization, Master's thesis, The graduate school yonsei university
  6. Yokota, T, Taguchi, T. and Gen, M, 'A Solution Method for Optimal Weight Design Problem of 10 Bar Truss Using Genetic Algorithms,' Computers ind. Engng, Vol. 35, Nos 1-2, pp. 367-372 https://doi.org/10.1016/S0360-8352(98)00096-5
  7. Sang Hun Lee, Kunwoo Lee, Chun Jin Go, 1988, 'An Integrated CAD System for Mold Design in Injection Molding Process,' The Korean Society of Mechanical Engineers, Vol.12 No.6, pp. 1227-1237
  8. Heon Young Kim, Joong Jae Kim, Young Joo Kim, 1997, 'The Injection Molding Analysis and The Mold Design for Automotive Plastic Fender,' Journal of the Korean Society for Technology of Plasticity, Vol.6 No.6, pp 489-499
  9. Seong Jin Park, Tai Hun Kwon, 1997, 'Optimum Design of Injection Molding Cooling System via Boundary Element Method,' The Korean Society of Mechanical Engineers(A), Vol. 21 No. 11, pp 1773-1785
  10. Jo, Y. M, Kwon, O. J, Yoo, Y. J. and Kim, J. D, 1995, 'A Study Estimation of Filling Phase Condition in Injection Molding Process,' Journal of the Korean Society of Precision Engineering, Vol. 12 No.3, pp. 110-118
  11. Saxena, M. and Irani, R. K, 1992, 'Automated Gating Plan Synthesis for Injection Molds,' Computers in Engineering, Vol. 1, pp. 381-389
  12. Pandelidis, l. and Zou, Q, 1990, 'Optimization of Injection Molding Design. (Part I : Gate Location Optimization),' Polymer Engineering and Science, Vol. 30, No. 15, pp. 873-882 https://doi.org/10.1002/pen.760301502
  13. Kim, B, Nam, G, Ryu, H. and Lee. J, 2000, 'Optimization of Filling Process in RTM Using Genetic Algorithm,' Korea-Australia Rheology Journal, Vol. 12, No. 1, March 2000, pp. 83-92
  14. 2000, Computer A ided Plastics Application-CAPA tutorial, VM-Tech
  15. Peter Kennedy, 1995, Flow Analysis of Injection Molds, Hanser/Gardner Publications

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  1. Gate Locations Optimization of an Automotive Instrument Panel for Minimizing Cavity Pressure vol.29, pp.6, 2012, https://doi.org/10.7736/KSPE.2012.29.6.648