Skip to main content
SpringerLink
Log in

An enhanced simulation-based design method coupled with meta-heuristic search algorithm for accurate reliability-based design optimization

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

The enhanced weighted simulation-based design method in conjunction with particle swarm optimization (PSO) is developed as a pseudo double-loop algorithm for accurate reliability-based design optimization (RBDO). According to this hybrid method, generated samples of weighed simulation method (WSM) are considered as initial population of the PSO. The proposed population is then employed to evaluate the safety level of each PSO swarm (design candidates) during movement. Using this strategy, there is no required to conduct new sampling for reliability assessment of design candidates (PSO swarms). Employing PSO as the search engine of RBDO and WSM as the reliability analyzer provide more accurate results with few samples and also increase the application range of traditional WSM. Besides, a shift strategy is also introduced to increase the capability of the WSM to investigate general RBDO problems including both deterministic and random design variables. Several examples are investigated to demonstrate the accuracy and robustness of the method. Results demonstrate the computational efficiency and superiority of the proposed method for practical engineering problems with highly nonlinear and implicit probabilistic constrains.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Mínguez R, Castillo E (2009) Reliability-based optimization in engineering using decomposition techniques and FORMS. Struct Saf 31:214–223

    Article  Google Scholar 

  2. Ahn J, Kim SH, Kwon JH (2005) Reliability-based wing design optimization using trust region-sequential quadratic programming framework. J Aircr 42(5):1331–1336

    Article  Google Scholar 

  3. Yang T, Hsieh YH (2013) Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization. Eng Comput 29:151–163

    Article  Google Scholar 

  4. Aoues Y, Chateauneuf A (2010) Benchmark study of numerical methods for reliability-based design optimization. Struct Multidiscip Optim 41(2):277–294

    Article  MathSciNet  MATH  Google Scholar 

  5. Hasofer AM, Lind NC (1974) Exact and invariant second moment code format. J Eng Mech Div 100(1):111–121

    Google Scholar 

  6. Du XP, Chen W (2001) A most probable point based method for efficient uncertainty analysis. J Des Manuf Autom 1(1–2):47–65

    Google Scholar 

  7. Nikolaidis E, Burdisso R (1998) Reliability-based optimization: a safety index approach. Comput Struct 28(6):781–788

    Article  MATH  Google Scholar 

  8. Tu J, Choi KK, Park YH (1999) A new study on reliability based design optimization. J Mech Des 121(4):557–564

    Article  Google Scholar 

  9. Reddy MV, Grandhi RV, Hopkins DA (1994) Reliability based structural optimization: a simplified safety index approach. Comput Struct 53(6):1407–1418

    Article  MATH  Google Scholar 

  10. Grandhi RV, Wang L (1998) Reliability-based structural optimization using improved two point adaptive nonlinear approximations. Fin Elem Anal Des 29(1):35–48

    Article  MATH  Google Scholar 

  11. Youn BD, Choi KK, Du L (2005) Enriched performance measure approach for reliability-based design optimization. AIAA J 43(4):874–884

    Article  Google Scholar 

  12. Yi P, Cheng GD, Jiang L (2008) A sequential approximate programming strategy for performance-measure-based probabilistic structural design optimization. Struct Saf 30:91–109

    Article  Google Scholar 

  13. Youn BD, Choi KK (2004) An investigation of nonlinearity of reliability-based design optimization approaches. J Mech Des 126(3):403–411

    Article  Google Scholar 

  14. Wu YT, Millwater HR, Cruse TA (1990) An advance probabilistic analysis method for implicit performance function. AIAA J 28(9):1663–1669

    Article  Google Scholar 

  15. Youn BD, Choi KK, Du L (2005) Adaptive probability analysis using an enhanced hybrid mean value method. Struct Multidisc Optim 29(2):134–148

    Article  Google Scholar 

  16. Du XP, Sudjianto A, Chen W (2004) An integrated framework for optimization under uncertainty using inverse reliability strategy. J Mech Des 126(4):562–570

    Article  Google Scholar 

  17. Allaix DL, Carbone VI (2015) An efficient coupling of FORM and Karhunen-Loève series expansion. Eng Comput. doi:10.1007/s00366-015-0394-1

    Google Scholar 

  18. Li F, Wu T, Hu M (2010) An accurate penalty-based approach for reliability-based design optimization. Res Eng Des 21(2):87–98

    Article  MathSciNet  Google Scholar 

  19. Chen Z, Qiu H, Gao L, Li P (2013) An adaptive decoupling approach for reliability-based design optimization. Comput Struct 117:58–66

    Article  Google Scholar 

  20. Pradlwarter HJ, Schuëller GI (2010) Local domain Monte Carlo simulation. Struct Saf 32:275–280

    Article  Google Scholar 

  21. Valdebenito MA, Pradlwarter HJ, Schuëller GI (2010) The role of the design point for calculating failure probabilities in view of dimensionality and structural nonlinearities. Struct Saf 32:101–111

    Article  Google Scholar 

  22. Dubourg V, Sudret B, Bourinet JM (2011) Reliability-based design optimization using kriging surrogate and subset simulation. Struct Multidiscip Optim 44(5):673–690

    Article  Google Scholar 

  23. Rashki M, Miri M, Azhdary Moghaddam M (2012) A new efficient simulation method to approximate the probability of failure and most probable point. Struct Saf 39:22–29

    Article  Google Scholar 

  24. Rashki M, Miri M, Azhdary Moghaddam M (2014) Closure to “A new efficient simulation method to approximate the probability of failure and most probable point”. Struct Saf 46:15–16

    Article  Google Scholar 

  25. Valdebenito MA, Schuëller GI (2010) A survey on approaches for reliability-based optimization. Struct Multidiscip Optim 42(5):645–663

    Article  MathSciNet  MATH  Google Scholar 

  26. Royset JO, Der Kiureghian A, Polak E (2001) Reliability-based optimal structural design by the decoupling approach. Reliab Eng Syst Saf 73(3):213–221

    Article  Google Scholar 

  27. Du X, Chen W (2004) Sequential optimization and reliability assessment method for efficient probabilistic design. J Mech Des 126:225–233

    Article  Google Scholar 

  28. Cho TM, Lee BC (2011) Reliability-based design optimization using convex linearization and sequential optimization and reliability assessment method. Struct Saf 33:42–50

    Article  Google Scholar 

  29. Valdebenito MA, Schuëller GI (2011) Efficient strategies for reliability-based optimization involving non-linear dynamical structures. Comput Struct 89:1797–1811

    Article  Google Scholar 

  30. Chen X, Hasselman TK, Neil DJ (1997) Reliability based structural design optimization for practical application. In: Proceeding of the 38th conference on structures, structural dynamics, and materials, AIAA/ASME/ASCE/AHS, AIAA 97-1403, kissimmee, FL

  31. Liang J, Mourelatos ZP, Nikolaidis E (2007) A single-loop approach for system reliability-based design optimization. J Mech Des 129(12):1215–1224

    Article  Google Scholar 

  32. Shan S, Wang GG (2008) Reliable design space and complete single-loop reliability-based design optimization. Reliab Eng Syst Saf 93(8):1218–1230

    Article  Google Scholar 

  33. Lee I, Choi KK, Du L, Gorsich D (2008) Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems. Comput Methods Appl Mech Eng 198(1):14–27

    Article  MATH  Google Scholar 

  34. Rashki M, Miri M, Azhdary Moghaddam M (2014) A simulation-based method for reliability based design optimization problems with highly nonlinear constraints. Autom Construct 47:24–36

    Article  Google Scholar 

  35. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, IEEE Service Center, Perth, Australia; pp 1942–1948

  36. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. Evolut Program VII:591–600

    Google Scholar 

  37. Yang JM, Chen YP, Horng JT, Kao CY (1997) Applying family competition to evolution strategies for constrained optimization. In: Lecture notes in computer science, vol 1213. Springer, Berlin, Heidelberg, New York, pp 201–211

  38. Homaifar A, Lai AHY, Qi X (1994) Constrained optimization via genetic algorithms. Simulation 62(4):242–254

    Article  Google Scholar 

  39. Kumar N, Vidyarthi DP (2015) A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems. Eng Comput. doi:10.1007/s00366-015-0396-z

    Google Scholar 

  40. Madsen HO (1988) Omission sensitivity factors. Struct Saf 5:35–45

    Article  MathSciNet  Google Scholar 

  41. Bjerager P (1990) Comments on: Henrik O. Madsen, Omission sensitivity factors. Struct Saf 7:77–79

    Article  Google Scholar 

  42. Hamed MM, Bedient PB (1997) On the effect of probability distributions of input variables in public health risk assessment. Risk Anal 17(1):97–105

    Article  Google Scholar 

  43. Lu XP, Li HL, Papalambros P (1984) A design procedure for the optimization of vehicle suspension. Int J Vehic Des 5(1–2):129–142

    Google Scholar 

  44. Hsu KS, Chan KY (2010) A filter-based sample average SQP for optimization problems with highly nonlinear probabilistic constraints. J Mech Des 132(11):111001

    Article  Google Scholar 

  45. Lee JJ, Lee BC (2005) Efficient evaluation of probabilistic constraints using an envelope function. Eng Optimiz 37(2):185–200

    Article  Google Scholar 

  46. Lee TH, Jung JJ (2008) A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO: constraint boundary sampling. Comput Struct 86(13–14):1463–1476

    Article  Google Scholar 

  47. Yi P, Cheng G (2008) Further study on efficiency of sequential approximate programming for probabilistic structural design optimization. Struct Multidiscip Optimiz 35(6):509–522

    Article  MathSciNet  MATH  Google Scholar 

  48. Shayanfar M, Abbasnia R, Khodam A (2014) Development of a GA-based method for reliability-based optimization of structures with discrete and continuous design variables using OpenSees and Tcl. Finite Elem Anal Des 90:61–73

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Sistan and Baluchestan, P.O. Box 9816745563-161, Zahedan, Iran

    Naser Safaeian Hamzehkolaei & Mahmoud Miri

  2. Department of Architecture, faculty of art and architecture, University of Sistan and Baluchestan, Zahedan, Iran

    Mohsen Rashki

Authors
  1. Naser Safaeian Hamzehkolaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud Miri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Rashki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmoud Miri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safaeian Hamzehkolaei, N., Miri, M. & Rashki, M. An enhanced simulation-based design method coupled with meta-heuristic search algorithm for accurate reliability-based design optimization. Engineering with Computers 32, 477–495 (2016). https://doi.org/10.1007/s00366-015-0427-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-015-0427-9

Keywords

Search

Navigation