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On Practical Automated Engineering Design

  • Lars NolleEmail author
  • Ralph Krause
  • Richard J. Cant
Chapter
First Online:
Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Abstract

In engineering, it is usually necessary to design systems as cheap as possible whilst ensuring that certain constraints are satisfied. Computational optimization methods can help to find optimal designs automatically. However, it is demonstrated in this work that an optimal design is often not robust against variations caused by the manufacturing process, which would result in unsatisfactory product quality. In order to avoid this, a meta-method is used in here, which can guide arbitrary optimization algorithms towards more robust solutions. This was demonstrated on a standard benchmark problem, the pressure vessel design problem, for which a robust design was found using the proposed method together with self-adaptive stepsize search, an optimization algorithm with only one control parameter to tune. The drop-out rate of a simulated manufacturing process was reduced by 30 % whilst maintaining near-minimal production costs, demonstrating the potential of the proposed method.

Keywords

Automated engineering design Robust solution Optimization Parameter tuning Self-adaptive stepsize search Pressure vessel problem 
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References

  1. 1.
    Arnaud R, Poirion F (2014) Stochastic annealing optimization of uncertain aeroelastic system. Aerosp Sci Technol 39:456–464CrossRefGoogle Scholar
  2. 2.
    Azad SK, Hasancebi O (2014) An elitist self-adaptive step-size search for structural design optimization. Appl Soft Comput 19:226–235CrossRefGoogle Scholar
  3. 3.
    Azad SK, Hasancebi O (2014) Elitist self-adaptive step-size search in optimum sizing of steel structures. Int J Adv Comput Sci Appl 4(4):192–196Google Scholar
  4. 4.
    Bach H (1969) On the downhill method. Commun ACM 12(12):675–677CrossRefzbMATHGoogle Scholar
  5. 5.
    Cao YJ, Wu QH (1999) A mixed variable evolutionary programming for optimization of mechanical design. Eng Intell Syst Electr Eng Commun 7(2):77–82Google Scholar
  6. 6.
    Christensen PW, Klarbring A (2008) An introduction to structural optimization. SpringerGoogle Scholar
  7. 7.
    Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Coello CAC, Montes EM (2001) Use of dominance-based tournament selection to handle constraints in genetic algorithms. Proc ANNIE 11:177–182Google Scholar
  9. 9.
    Congedo PM, Witteveen J, Iaccarino G (2013) A simplex-based numerical framework for simple and efficient robust design optimization. Comput Optim Appl 56:231–251MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Costanzo F (2013) Engineering mechanics: statics, 2nd edn. McGraw-Hill CompaniesGoogle Scholar
  11. 11.
    Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2–4):311–338CrossRefzbMATHGoogle Scholar
  12. 12.
    Dias Junior A, da Silva Junior DC (2013) Using guiding heuristics to improve the dynamic checking of temporal properties in data dominated high-level designs. In: Proceedings of IEEE Computer Society Annual Symposium on VLSI, pp 20–25Google Scholar
  13. 13.
    Dimopoulos GG (2007) Mixed-variable engineering optimization based on evolutionary and social metaphors. Comput Methods Appl Mech Eng 196(4–6):803–817CrossRefzbMATHGoogle Scholar
  14. 14.
    Dorigo M, Gambardella L (1997) Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
  15. 15.
    El-Mihoub T, Hopgood AA, Nolle L, Battersby A (2006) Hybrid genetic algorithms: a review. Eng Lett 13(2):124–137Google Scholar
  16. 16.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-WesleyGoogle Scholar
  17. 17.
    Guo WA, Li WZ, Zhang Q, Wang L, Wu QD, Ren HL (2015) Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems. Eng Optim 46(11):1465–1484MathSciNetCrossRefGoogle Scholar
  18. 18.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan PressGoogle Scholar
  19. 19.
    He S, Prempain E, Wu QH (2004) An improved particle swarm optimiser for mechanical design optimization problems. Eng Optim 36(5):585–605MathSciNetCrossRefGoogle Scholar
  20. 20.
    Jamshidi R, Ghomi SMTF, Karimi B (2015) Flexible supply chain optimization with controllable lead time and shipping option. Appl Soft Comput 30:26–35CrossRefGoogle Scholar
  21. 21.
    Jayaprakasam S, Rahim SKA, Leow CY (2015) PSOGSA-explore: a new hybrid metaheuristic approach for beam pattern optimization in collaborative beamforming. Appl Soft Comput 30:229–237CrossRefGoogle Scholar
  22. 22.
    Jeet V, Kutanoglu E (2007) Lagrangian relaxation guided problem space search heuristics for generalized assignment problems. Eur J Oper Res 182(3):1039–1056CrossRefzbMATHGoogle Scholar
  23. 23.
    Kanagaraj G, Ponnambalam SG, Jawahar N, Nilakantan Mukund J (2015) An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization. Eng Optim 46(10): 1331–1351Google Scholar
  24. 24.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol. 4, pp 1942–1948Google Scholar
  25. 25.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1984) Optimization by simulated annealing: quantitative study. J Stat Phys 34(1984):975–986CrossRefzbMATHGoogle Scholar
  26. 26.
    Kitayama S, Arakawa M, Yamazaki K (2006) Penalty function approach for the mixed discrete nonlinear problems by particle swarm optimization. Struct Multidiscip Optim 32(3):191–202Google Scholar
  27. 27.
    Li Z, Li Ze, Nguyen TT, Chen S (2015) Orthogonal chemical reaction optimization algorithm for global numerical optimization problems. Expert Syst Appl 42:3242–3252CrossRefGoogle Scholar
  28. 28.
    Li X, Zhang G (2013) Minimum penalty for constrained evolutionary optimization. Comput Optim Appl 60(2):513–544MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Liao T, Socha K, Montes de Oca MA, Stuetzle T, Dorigo M (2014) Ant colony optimization for mixed-variable optimization problems. IEEE Trans Evol Comput 18(4):503–518CrossRefGoogle Scholar
  30. 30.
    Lopez RH, Ritto TG, Sampaio R, Souza de Cursi JE (2014) A new algorithm for the robust optimization of rotor-bearing systems. Eng Optim 46(8):1123–1138CrossRefGoogle Scholar
  31. 31.
    Mahia M, Baykan ÖK, Kodaz H (2015) A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl Soft Comput 30:484–490CrossRefGoogle Scholar
  32. 32.
    Martínez-Soto R, Castillo O, Aguilar LT, Rodriguez A (2015) A hybrid optimization method with PSO and GA to automatically design Type-1 and Type-2 fuzzy logic controllers. Int J Mach Learn Cybernet 6:175–196CrossRefGoogle Scholar
  33. 33.
    Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092CrossRefGoogle Scholar
  34. 34.
    Murty KG (1983) Linear programming, John Wiley & SonsGoogle Scholar
  35. 35.
    Nelder JA, Mead R (1965) A simplex-method for function minimization. Comput J 7(4):308–313CrossRefzbMATHGoogle Scholar
  36. 36.
    Nema S, Goulermas J, Sparrow G, Cook P (2008) A hybrid particle swarm branch-and-bound (HPB) optimizer for mixed discrete nonlinear programming. IEEE Trans Syst Man Cybern Part A 38(6):1411–1424CrossRefGoogle Scholar
  37. 37.
    Nolle L (2006) On a hill-climbing algorithm with adaptive step size: towards a control parameter-less black-box optimisation Algorithm. In: Reusch B (ed) Computational intelligence, theory and applications, advances in soft computing, vol 38. Springer, pp 587–595Google Scholar
  38. 38.
    Nolle L (2007) SASS applied to optimum work roll profile selection in the hot rolling of wide steel. Knowl-Based Syst 20(2):203–208CrossRefGoogle Scholar
  39. 39.
    Nolle L, Bland JA (2012) Self-adaptive stepsize search for automatic optimal design. Knowl-Based Syst 29:75–82CrossRefGoogle Scholar
  40. 40.
    OED (2015) Oxford English dictionary. Oxford University PressGoogle Scholar
  41. 41.
    Pappas M, Amba-Rao CL (1971) A direct search algorithm for automated optimum structural design. Am Inst Aeron Astron J 9(3):387–393CrossRefGoogle Scholar
  42. 42.
    Pholdee N, Park W, Kim DK, Im Y, Bureerat S, Kwon H, Chun M (2015) Efficient hybrid evolutionary algorithm for optimization of a strip coiling process. Eng Optim 47(4), 521–532Google Scholar
  43. 43.
    Pullarcot S (2002) Practical guide to pressure vessel manufacturing. CRC PressGoogle Scholar
  44. 44.
    Rao SS (2009) Engineering optimization, theory and practice, 4th edn. WileyGoogle Scholar
  45. 45.
    Rechenberg I (1973) Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien derbiologischen Evolution, Frommann-HolzboogGoogle Scholar
  46. 46.
    Rubinstein RY (1981) Simulation and the Monte Carlo method. Wiley, New YorkCrossRefzbMATHGoogle Scholar
  47. 47.
    Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3):284–294CrossRefGoogle Scholar
  48. 48.
    Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18CrossRefGoogle Scholar
  49. 49.
    Sandgren S (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112:223–229CrossRefGoogle Scholar
  50. 50.
    Sayol J, Nolle L, Schaefer G, Nakashima T (2008) Comparison of simulated annealing and SASS for parameter estimation of biochemical networks. In: Proceedings of IEEE World Congress on Computational Intelligence, 1–6 June, Hong Kong, China, pp 3568–3571Google Scholar
  51. 51.
    Shanley FR (1949) Principles of structural design for minimum weight. J Aeron Sci 16(3):133–149CrossRefGoogle Scholar
  52. 52.
    Standards Australia (1995) Steel plates for pressure equipment AS 1548:1995Google Scholar
  53. 53.
    Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetCrossRefzbMATHGoogle Scholar
  54. 54.
    Templeman AB (1970) Structural design or minimum cost using the method of geometric programming. ICE Proc. 46(4):459–472Google Scholar
  55. 55.
    Wang J, Yuan W, Cheng D (2015) Hybrid genetic–particle swarm algorithm: an efficient method for fast optimization of atomic clusters. Comput Theor Chem 1059:12–17CrossRefGoogle Scholar
  56. 56.
    Xu R, Venayagamoorthy GK, Wunsch DC (2007) Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization. Neural Netw 20(8):917–992CrossRefzbMATHGoogle Scholar
  57. 57.
    Yang HZ, Zhu Y, Lu QJ, Zhang J (2015) Dynamic reliability based design optimization of the tripod sub-structure of offshore wind turbines. Renew Energy 78:16–25CrossRefGoogle Scholar
  58. 58.
    Zhou Y, Zhou G, Zhang J (2015) A hybrid glow worm swarm optimization algorithm to solve constrained multimodal functions optimization. Optim: J Math Progr Oper Res 64(4), 1057–1080Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Engineering Science, WE Applied Computer ScienceJade University of Applied ScienceWilhelmshavenGermany
  2. 2.Siemens AGEnergy ManagementErlangenGermany
  3. 3.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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