Structural and Multidisciplinary Optimization

, Volume 41, Issue 3, pp 481–494 | Cite as

An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

Industrial Application

Abstract

A particle swarm optimization (PSO) solver is developed based on theoretical information available from the literature. The implementation is validated by utilizing the PSO optimizer as a driver for a single discipline optimization and for a multicriterion optimization and comparing the results to a commercially available gradient based optimization algorithm, previously published results, and a simple sequential Monte Carlo model. A typical conceptual ship design statement from the literature is employed for developing the single discipline and the multicriterion benchmark optimization statements. In the main new effort presented in this paper, an approach is developed for integrating the PSO algorithm as a driver at both the top and the discipline levels of a multidisciplinary design optimization (MDO) framework which is based on the Target Cascading (TC) method. The integrated MDO/PSO algorithm is employed for analyzing a multidiscipline optimization statement reflecting the conceptual ship design problem from the literature. Results are compared to MDO analyses performed when a gradient based optimizer comprised the optimization driver at all levels. The results, the strengths, and the weaknesses of the integrated MDO/PSO algorithm are discussed as related to conceptual ship design.

Keywords

Metaheuristic Particle swarm optimization Multidisciplinary design optimization Ship design 

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References

  1. Alexandrov NM, Hussaini MY (1997) Multidisciplinary design optimization: state of the art. Society for Industrial & Applied Mathematics, PhiladelphiaGoogle Scholar
  2. Ali M, Kaelo P (2008) Improved particle swarm algorithms for global optimization. Appl Math Comput 196(2):578–593MATHCrossRefMathSciNetGoogle Scholar
  3. Berends J, van Tooren MJL et al (2006) A distributed multi-disciplinary optimisation of a blended wing body UAV using a multi-agent task environment. In: 47th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, pp 1–22Google Scholar
  4. Bochenek B, Forys P (2006) Structural optimization for post-buckling behavior using particle swarms. Struct Multidisc Optim 32(6):521–531CrossRefGoogle Scholar
  5. Campana E, Fasano G et al (2006a) Dynamic system analysis and initial particles position in particle swarm optimizationGoogle Scholar
  6. Campana EF, Fasano G et al (2006b) Particle swarm optimization: efficient globally convergent modifications. In: Proceedings of the III European conference on computational mechanics, solids, structures and coupled problems in engineering, Lisbon, PortugalGoogle Scholar
  7. Chiba K, Makino Y et al (2007) Multidisciplinary design exploration of wing shape for silent supersonic technology demonstrator. AIAA Pap 4167:2007-6Google Scholar
  8. Chiba K, Makino Y et al (2008) PSO/GA hybrid method and its application to supersonic-transport wing design. J Comput Sci Technol 2(1):268–280CrossRefGoogle Scholar
  9. Cox SE, Haftka RT et al (2001) A comparison of global optimization methods for the design of a high-speed civil transport. J Glob Optim 21(4):415–432MATHCrossRefMathSciNetGoogle Scholar
  10. Craig KJ, Kingsley TC (2007) Design optimization of containers for sloshing and impact. Struct Multidisc Optim 33(1):71–87CrossRefGoogle Scholar
  11. Cramer EJ, Dennis JE et al (1994) Problem formulation for multidisciplinary optimization. SIAM J Optim 4(4):754–776MATHCrossRefMathSciNetGoogle Scholar
  12. Demko D (2005) Tools for multi-objective and multi-disciplinary optimization in naval ship design. Thesis, Virginia Polytechnic Institute and State UniversityGoogle Scholar
  13. Fletcher R (1987) Practical methods of optimization. Wiley-Interscience, New YorkMATHGoogle Scholar
  14. Floudas CA, Pardalos PM (2001) Encyclopedia of optimization. Kluwer, DordrechtMATHCrossRefGoogle Scholar
  15. Fourie PC, Groenwold AA (2002) The particle swarm optimization algorithm in size and shape optimization. Struct Multidisc Optim 23(4):259–267CrossRefGoogle Scholar
  16. Frank PD, Booker AJ et al (1992) A comparison of optimization and search methods for multidisciplinary design. AIAA Pap 92-4827Google Scholar
  17. Giesing JP, Jean-Francois MB (1998) A summary of industry MDO applications and needs. Symposium on multidisciplinary analysis and optimizationGoogle Scholar
  18. He J, Zhang G et al (2008) Uncertainty propagation in multi-disciplinary design optimization of undersea vehicles. SAE Paper 2008-01-0218Google Scholar
  19. Hulme KF, Bloebaum CL (2000) A simulation-based comparison of multidisciplinary design optimization solution strategies using CASCADE. Struct Multidisc Optim 19(1):17–35CrossRefGoogle Scholar
  20. Idahosa U, Golubev VV et al (2005). Application of distributed automated MDO environment to aero/acoustic shape optimization of a fan blade. In: 11th AIAA/CEAS aeroacoustics conference (26th aeroacoustics conference), pp 1–14Google Scholar
  21. Jansson JO, Shneerson D (1982) The optimal ship size. J Transp Econ Policy 16:217–238Google Scholar
  22. Jouhaud JC, Sagaut P et al (2007) A surrogate-model based multidisciplinary shape optimization method with application to a 2D subsonic airfoil. Comput Fluids 36(3):520–529MATHCrossRefGoogle Scholar
  23. Karakasis MK, Giannakoglou KC (2006) On the use of metamodel-assisted, multi-objective evolutionary algorithms. Eng Optim 38(8):941–957CrossRefMathSciNetGoogle Scholar
  24. Kendall PMH (1972) A theory of optimum ship size. J Transp Econ Policy 6:128–146Google Scholar
  25. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4Google Scholar
  26. Kim HM (2001) Target cascading in optimal system design. PhD thesis, Mechanical Engineering, University of Michigan, Ann ArborGoogle Scholar
  27. Kim IY, de Weck OL (2005) Adaptive weighted-sum method for bi-objective optimization: pareto front generation. Struct Multidisc Optim 29(2):149–158CrossRefGoogle Scholar
  28. Kim HM, Kokkolaras M et al (2002) Target cascading in vehicle redesign: a class VI truck study. Int J Veh Des 29(3):199–225CrossRefGoogle Scholar
  29. Kim H, Rideout D et al (2003a) Analytical target cascading in automotive vehicle design. Trans Am Soc Mech Eng J Mech Des 125(3):481–489Google Scholar
  30. Kim HM, Michelena NF et al (2003b) Target cascading in optimal system design. J Mech Des (Trans ASME) 125(3):474–480CrossRefGoogle Scholar
  31. Koh B, George A et al (2006) Parallel asynchronous particle swarm optimization. Int J Numer Methods Eng 67:578–595MATHCrossRefGoogle Scholar
  32. Kokkolaras M, Fellini R et al (2002) Extension of the target cascading formulation to the design of product families. Struct Multidisc Optim 24(4):293–301CrossRefGoogle Scholar
  33. Lee D (1999) Hybrid system approach to optimum design of a ship. AI EDAM 13(01):1–11CrossRefGoogle Scholar
  34. Lewis K (2002) Multidisciplinary design optimization. Aerosp Am 40(12):42Google Scholar
  35. Merkle D, Middendorf M et al (2002) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput 6(4):333–346CrossRefGoogle Scholar
  36. Michelena N, Park H et al (2003) Convergence properties of analytical target cascading. AIAA J 41(5):897–905CrossRefGoogle Scholar
  37. Mistree F, Smith WF et al (1990) Decision-based design: a contemporary paradigm for ship design. Trans Soc Naval Arch Marine Eng 98:565–597Google Scholar
  38. Moles CG, Mendes P et al (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13(11):2467–2474CrossRefGoogle Scholar
  39. Moraes HB, Vasconcellos JM et al (2007) Multiple criteria optimization applied to high speed catamaran preliminary design. Ocean Eng 34(1):133–147CrossRefGoogle Scholar
  40. Neu WL, Hughes O et al (2000) A prototype tool for multidisciplinary design optimization of ships. In: Ninth congress of the international maritime association of the Mediterranean, Naples, ItalyGoogle Scholar
  41. Papalambros PY, Wilde DJ (2000) Principles of optimal design. Cambridge University Press, CambridgeMATHCrossRefGoogle Scholar
  42. Parashar S, Bloebaum CL (2006) Multi-objective genetic algorithm concurrent subspace optimization (MOGACSSO) for multidisciplinary design. In: 47th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, pp 1–11Google Scholar
  43. Parsons MG, Scott RL (2004) Formulation of multicriterion design optimization problems for solution with scalar numerical optimization methods. J Ship Res 48(1):61–76Google Scholar
  44. Parsons MG, Singer DJ et al (1999) A hybrid agent approach for set-based conceptual ship design. In: Proceedings of the international conference on computer applications in shipbuilding, Cambridge, MA, pp 7–11Google Scholar
  45. Peri D, Campana EF (2003a) High fidelity models in the multi-disciplinary optimization of a frigate ship. In: 2nd MIT conference on fluid and solid mechanics, CambridgeGoogle Scholar
  46. Peri D, Campana EF (2003b) Multidisciplinary design optimization of a naval surface combatant. J Ship Res 47(1):1–12Google Scholar
  47. Peri D, Campana EF (2005) High-fidelity models for multiobjective global optimization in simulation-based design. J Ship Res 49(3):159–175Google Scholar
  48. Peri D, Campana EF et al (2001a) Development of CFD-based design optimization architecture. In: 1st MIT conference on fluid and solid mechanics, CambridgeGoogle Scholar
  49. Peri D, Rossetti M et al (2001b) Design optimization of ship hulls via CFD techniques. J Ship Res 45(2):140–149Google Scholar
  50. Pinto A, Peri D et al (2007) Multiobjective optimization of a containership using deterministic particle swarm optimization. J Ship Res 51(3):217–228Google Scholar
  51. Poli R, Kennedy J et al (2008) Editorial: particle swarms: the second decade. J Artif Evol Appl 2008(3)Google Scholar
  52. Ray T, Gokarn RP et al (1995) A global optimization model for ship design. Comput Ind 26(2):175–192CrossRefGoogle Scholar
  53. Schutte J, Reinbolt J et al (2004) Parallel global optimization with the particle swarm algorithm. Int J Numer Meth Eng 61:2296–2315MATHCrossRefGoogle Scholar
  54. Sen P, Yang JB (1998) Multiple criteria decision support in engineering design. Springer, New YorkGoogle Scholar
  55. Serra M, Venini P (2006) On some applications of ant colony optimization metaheuristic to plane truss optimization. Struct Multidisc Optim 32(6):499–506CrossRefGoogle Scholar
  56. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. Evol Program 7:611–616Google Scholar
  57. Shi Y, Eberhart RC et al (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation.CEC 99, vol 3Google Scholar
  58. Shi Y, Eberhart RC et al (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 congress on evolutionary computation, vol 1Google Scholar
  59. Sinha K (2007) Reliability-based multiobjective optimization for automotive crashworthiness and occupant safety. Struct Multidisc Optim 33(3):255–268CrossRefGoogle Scholar
  60. Statnikov RB, Matusov JB (1995) Multicriteria optimization and engineering. Chapman & Hall, New YorkGoogle Scholar
  61. Sun J, Zhang G et al (2006) Multi-disciplinary design optimization under uncertainty for thermal protection system applications. AAIA Paper 2006-7002-547Google Scholar
  62. Tahara Y, Tohyama S et al (2006) CFD-based multi-objective optimization method for ship design. Int J Numer Methods Fluids 52(5):499MATHCrossRefGoogle Scholar
  63. Tosserams S, Etman LFP et al (2006) An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers. Struct Multidisc Optim 31(3):176–189CrossRefMathSciNetGoogle Scholar
  64. van den Bergh F, Engelbrecht A (2006) A study of particle swarm optimization particle trajectories. Inf Moksl 176(8):937–971MATHGoogle Scholar
  65. Venkayya VB (1989) Optimality criteria: a basis for multidisciplinary design optimization. Comput Mech 5(1):1–21MATHCrossRefGoogle Scholar
  66. Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA J 41(8):1583–1589CrossRefGoogle Scholar
  67. Venter G, Sobieszczanski-Sobieski J (2004) Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Struct Multidisc Optim 26(1):121–131CrossRefGoogle Scholar
  68. Venter G, Sobieszczanki-Sobieksi J (2006) A parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J Aero Comput Inform Commun 3:123–137CrossRefGoogle Scholar
  69. Vianese J (2004) Multidisciplinary optimization of naval ship design and mission effectiveness, vol 85. Defense Technical Information Center, US Dept of Defense, Washington, DCGoogle Scholar
  70. Villagra M, Baran B (2007) Ant colony optimization with adaptive fitness function for satisfiability testing. Lect Notes Comput Sci 4576:352CrossRefMathSciNetGoogle Scholar
  71. Vlahopoulos N, Wang A et al (2008) Engaging structural-acoustic simulations in multi-discipline optimization. In: NOISE-CON 2008, Dearborn, MIGoogle Scholar
  72. Wang J, Yin Z (2008) A ranking selection-based particle swarm optimizer for engineering design optimization problems. Struct Multidisc Optim 37(2):131–147CrossRefGoogle Scholar
  73. Yang YS, Park CK et al (2007) A study on the preliminary ship design method using deterministic approach and probabilistic approach including hull form. Struct Multidisc Optim 33(6):529–539CrossRefMathSciNetGoogle Scholar
  74. Yi SI, Shin JK et al (2008) Comparison of MDO methods with mathematical examples. Struct Multidisc Optim 35(5):391–402CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Naval Architecture and Marine Engineering DepartmentStephen M. Ross School of BusinessAnn ArborUSA
  2. 2.Naval Architecture and Marine Engineering Department, Mechanical Engineering Department, College of EngineeringUniversity of MichiganAnn ArborUSA

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