Sequential Approximate Optimization using Radial Basis Function network for engineering optimization

Optimization and Engineering

Volumn 12, Issue 4, 2011, Pages 535-557

  Kitayama, Satoshi ^a   Arakawa, Masao ^b   Yamazaki, Koetsu ^a  

^a KANAZAWA UNIVERSITY   (Japan)

^b KAGAWA UNIVERSITY   (Japan)

Author keywords

Density function; Engineering optimization; RBF network; Response surface; Sequential approximate optimization

Indexed keywords

ADAPTIVE SCALING; BASIS FUNCTIONS; ENGINEERING OPTIMIZATION; GAUSSIAN FUNCTIONS; GLOBAL APPROXIMATION; GLOBAL MINIMA; LOCAL APPROXIMATION; METAMODELING; NUMERICAL EXAMPLE; RBF NETWORK; RESPONSE SURFACE; SAMPLING POINTS; SAMPLING STRATEGIES; SEQUENTIAL APPROXIMATE OPTIMIZATION; SIMPLE METHOD; SUFFICIENT CONDITIONS;

GAUSSIAN DISTRIBUTION; NUMERICAL METHODS; OPTIMIZATION; RADIAL BASIS FUNCTION NETWORKS; SURFACE PROPERTIES;

FUNCTION EVALUATION;

EID: 80053960390     PISSN: 13894420     EISSN: 15732924     Source Type: Journal    
DOI: 10.1007/s11081-010-9118-y     Document Type: Article

References (31)

1. An W, Sun Y (2006) An equivalence between SILF-SVR and ordinary Kriging. Neural Process Lett 23: 133-141.
2. Andras P (2002) The equivalence of support vector machine and regularization neural networks. Neural Process Lett 65: 97-104.
3. Arora JS (1989) Introduction to optimum design. McGraw-Hill, New York.
4. Coello Coello CA (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41: 113-127.
5. Donald J, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13: 455-492.
6. Fang H, Horstemeyer MF (2006) Global response approximation with radial basis function. Eng Optim 38(4): 407-424.
7. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan Co, New York, pp 236-284.
8. Hu XH, Eberhart RC, Shi YH (2003) Engineering optimization with particle swarm. In: IEEE swarm intelligence symposium, pp 53-57.
9. Huang D, Allen TT, Notz WI, Zeng N (2006) Global optimization of stochastic black-box systems via sequential Kriging meta-models. J Glob Optim 34: 441-466.
10. Hussain MF, Barton RR, Joshi SB (2002) Metamodeling: radial basis functions, versus polynomials. Eur J Oper Res 138: 142-154.
11. Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidiscip Optim 23: 1-13.
12. Kurtaran H, Eskandarian A, Marzougui D, Bedewi NE (2002) Crashworthiness design optimization using successive response surface approximations. Comput Mech 29: 409-421.
13. Martin JD, Simpson TW (2005) Use of Kriging models to approximate deterministic computer models. AIAA J 43(4): 853-863.
14. McDonald DB, Grantham WJ, Tabor WL, Murphy MJ (2007) Global and local optimization using radial basis function response surface models. Appl Math Model 31: 2095-2110.
15. Muller AA, Messac A (2005) Extended radial basis functions: more flexible and effective metamodeling. AIAA J 43(6): 1306-1315.
16. Myers RH, Montgomery DH (1995) Response surface methodology. Wiley, New York.
17. Nakayama H, Arakawa M, Sasaki R (2002) Simulation-based optimization using computational intelligence. Optim Eng 3: 201-214.
18. Orr MJL (1996) Introduction to radial basis function networks. http://www. anc. ed. ac. uk/rbf/rbf. html (On-line available).
19. Perez VM, Renaud JE, Watson LT (2002) Adaptive experimental design for construction of response surface approximations. AIAA J 40(12): 2495-2503.
20. Poggio T, Girosi F (1990) Networks for approximation and learning. Proc IEEE 78(9): 1481-1497.
21. Ray T, Saini P (2001) Engineering design optimization using swarm with an intelligent information sharing among individuals. Eng Optim 33: 735-748.
22. Rodriguez JF, Perez VM, Padmanabhan D, Renaud JE (2001) Sequential approximate optimization using variable fidelity response surface approximations. Struct Multidiscip Optim 22: 24-34.
23. Sasena MJ, Papalambros PY, Goovaerts P (2002a) Exploration of metamodeling sampling criteria for constrained global optimization. Eng Optim 34(3): 263-278.
24. Sasena MJ, Papalambros PY, Goovaerts P (2002b) Global optimization of problems with disconnected feasible regions via surrogate modeling. In: 9th/ISSMO symposium on multidisciplinary analysis and optimization, AIAA2002-5573.
25. Sharif B, Wang GG, EiMekkawy TY (2008) Mode pursuing sampling method for discrete variable optimization on expensive black-box functions. J Mech Des 130: 021402.
26. Simpson TW, Mistree F (2001) Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J 30(12): 2233-2241.
27. Simpson TW, Mauery TM, Korte JJ, Mistree F (2001a) Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J 39(12): 2233-2241.
28. Simpson TW, Peplinski JD, Kock PN, Allen JK (2001b) Metamodels for computer-based engineering design: survey and recommendations. Eng Comput 17: 129-150.
29. Sobester A, Leary SJ, Keane AJ (2005) On the design of optimization strategies based on global response surface approximation models. J Glob Optim 33: 31-59.
30. Wang GG (2003) Adaptive response surface method using inherited Latin hypercube design points. J Mech Des 125: 210-220.
31. Wang GG, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Des 129: 370-380.