Optimal Design of Alloy Steels Using Multiobjective Genetic Algorithms

REFERENCES

• Tenner , J. Optimisation of the Heat Treatment of Steel using Neural Networks . In Department of Automatic Control and Systems Engineering. PhD thesis , Sheffield , The University of Sheffield , 1999 .
   Google Scholar
• Jones , J. ; MacKay , D.J.C. Neural Network Modelling of the Mechanical Properties of Nickel Base Superalloys , 8th Int. Symposium on Superalloys , Seven Springs , PA ; et al. Eds.; TMS Publications , 1996 ; 417 – 424 .
   Google Scholar
• Badmos , Y. ; Bhadeshia , H.K.D.H. ; MacKay , D.J.C. Tensile properties of mechanically alloyed oxide dispersion strengthened iron alloys. Part I neural network models . Mater. Sci. Technol. 1998 , 14 , 793 – 809 . [CSA]
   Web of Science ®Google Scholar
• Dulikravich , G. S. ; Egorov , I. N. ; Sikka , V. K. ; Muralidharan , G. Semistochastic Optimisation of Chemical Composition of High Temperature Austenitic Steels for Desired Emchanical Properties , 2003 TMS Annual Meeting, Yazawa International Symposium: Processing and Technologies , San Diego , CA , Kongoli , F. ; Itakagi , K. ; Yamaguchi , C. ; Sohn , H.-Y. Eds.; TMS Publications , 2003 ; Vol. 1 , 801 – 814 .
   Google Scholar
• Yegorov-Egorov , I.N. ; Dulikravich , G.S. Inverse Design of Alloys for Specified Stress, Temperature and Time-to-Rupture by Using Stochastic Optimisation . Inverse Problems, Design and Optimisation Symposium , Rio de Janeiro , Brazil , 2004 .
   Google Scholar
• Zitzler , E. ; Thiele , L. Multi-objective evolutionary algorithms: A comparitive case study and the strength Pareto approach. IEEE Trans. Evolut. Comput. 1999, 3, 257–271. [CSA] [CROSSREF]
   Web of Science ®Google Scholar
• Zitzler , E. ; Laumanns , M. ; Thiele , L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multi-Objective Optimisation . In Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems . Proceedings of the EUROGEN2001 Conference ; et al. Eds.; International Center for Numerical Methods in Engineering (CIMNE) : Barcelona , Spain , 2002 ; 95 – 100 .
   Google Scholar
• Goldberg , D.E. , Genetic Algorithms in Search, Optimisation, and Machine Learning ; Addison Wesley : Wokingham , 1989 .
   Google Scholar
• Mahfouf , M. ; Linkens , D.A. ; Abbod , M.F. Optimisation of Steel Production Incorporating Economic Factors 15th IFAC World Congress on Automatic Control Barcelona (Spain) ; Camacho , E.F. Eds.; Pergamon Press : Oxford ( UK ), 2002 .
   Google Scholar
• Jones , R. M. Introduction to MFC Programming with Visual C++ ; Prentice Hall PTR : Upper Saddle River , NJ , 2000 .
   Google Scholar
• Prosise , J. Programming Windows with MFC , 2nd Ed. , Microsoft Press : Redmond , WA , 1999 .
   Google Scholar
• Horn , J. Multi-criterion decision making . In Handbook of Evolutionary Computation , Section F1.9 ; et al. Eds.; Bristol : Institute of Physics Pub., Oxford University Press , 1997 .
   Google Scholar
• Tamaki , H. ; Kita , H. ; Kobayashi , S. Multi-objective Optimisation by Genetic Algorithms: A Review . Proc. IEEE International Conference on Evolutionary Computation (ICEC'96) , IEEE : New York , 1996 , 517 – 522 .
   Google Scholar
• Coello , C.A.C. A comprehensive survey of evolutionary based multi-objective optimisation techniques . Knowl. Informa. Systems 1999 , 1 , 269 – 308 .
   Google Scholar