An integrated multidisciplinary particle swarm optimization approach to conceptual ship design
 Christopher G. Hart,
 Nickolas Vlahopoulos
 … show all 2 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
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.
 Alexandrov, NM, Hussaini, MY (1997) Multidisciplinary design optimization: state of the art. Society for Industrial & Applied Mathematics, Philadelphia
 Ali, M, Kaelo, P (2008) Improved particle swarm algorithms for global optimization. Appl Math Comput 196: pp. 578593 CrossRef
 Berends J, van Tooren MJL et al (2006) A distributed multidisciplinary optimisation of a blended wing body UAV using a multiagent task environment. In: 47th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, pp 1–22
 Bochenek, B, Forys, P (2006) Structural optimization for postbuckling behavior using particle swarms. Struct Multidisc Optim 32: pp. 521531 CrossRef
 Campana E, Fasano G et al (2006a) Dynamic system analysis and initial particles position in particle swarm optimization
 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, Portugal
 Chiba K, Makino Y et al (2007) Multidisciplinary design exploration of wing shape for silent supersonic technology demonstrator. AIAA Pap 4167:20076
 Chiba, K, Makino, Y (2008) PSO/GA hybrid method and its application to supersonictransport wing design. J Comput Sci Technol 2: pp. 268280 CrossRef
 Cox, SE, Haftka, RT (2001) A comparison of global optimization methods for the design of a highspeed civil transport. J Glob Optim 21: pp. 415432 CrossRef
 Craig, KJ, Kingsley, TC (2007) Design optimization of containers for sloshing and impact. Struct Multidisc Optim 33: pp. 7187 CrossRef
 Cramer, EJ, Dennis, JE (1994) Problem formulation for multidisciplinary optimization. SIAM J Optim 4: pp. 754776 CrossRef
 Demko D (2005) Tools for multiobjective and multidisciplinary optimization in naval ship design. Thesis, Virginia Polytechnic Institute and State University
 Fletcher, R (1987) Practical methods of optimization. WileyInterscience, New York
 Floudas, CA, Pardalos, PM (2001) Encyclopedia of optimization. Kluwer, Dordrecht CrossRef
 Fourie, PC, Groenwold, AA (2002) The particle swarm optimization algorithm in size and shape optimization. Struct Multidisc Optim 23: pp. 259267 CrossRef
 Frank PD, Booker AJ et al (1992) A comparison of optimization and search methods for multidisciplinary design. AIAA Pap 924827
 Giesing JP, JeanFrancois MB (1998) A summary of industry MDO applications and needs. Symposium on multidisciplinary analysis and optimization
 He J, Zhang G et al (2008) Uncertainty propagation in multidisciplinary design optimization of undersea vehicles. SAE Paper 2008010218
 Hulme, KF, Bloebaum, CL (2000) A simulationbased comparison of multidisciplinary design optimization solution strategies using CASCADE. Struct Multidisc Optim 19: pp. 1735 CrossRef
 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 1–14
 Jansson, JO, Shneerson, D (1982) The optimal ship size. J Transp Econ Policy 16: pp. 217238
 Jouhaud, JC, Sagaut, P (2007) A surrogatemodel based multidisciplinary shape optimization method with application to a 2D subsonic airfoil. Comput Fluids 36: pp. 520529 CrossRef
 Karakasis, MK, Giannakoglou, KC (2006) On the use of metamodelassisted, multiobjective evolutionary algorithms. Eng Optim 38: pp. 941957 CrossRef
 Kendall, PMH (1972) A theory of optimum ship size. J Transp Econ Policy 6: pp. 128146
 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4
 Kim HM (2001) Target cascading in optimal system design. PhD thesis, Mechanical Engineering, University of Michigan, Ann Arbor
 Kim, IY, Weck, OL (2005) Adaptive weightedsum method for biobjective optimization: pareto front generation. Struct Multidisc Optim 29: pp. 149158 CrossRef
 Kim, HM, Kokkolaras, M (2002) Target cascading in vehicle redesign: a class VI truck study. Int J Veh Des 29: pp. 199225 CrossRef
 Kim, H, Rideout, D (2003) Analytical target cascading in automotive vehicle design. Trans Am Soc Mech Eng J Mech Des 125: pp. 481489
 Kim, HM, Michelena, NF (2003) Target cascading in optimal system design. J Mech Des (Trans ASME) 125: pp. 474480 CrossRef
 Koh, B, George, A (2006) Parallel asynchronous particle swarm optimization. Int J Numer Methods Eng 67: pp. 578595 CrossRef
 Kokkolaras, M, Fellini, R (2002) Extension of the target cascading formulation to the design of product families. Struct Multidisc Optim 24: pp. 293301 CrossRef
 Lee, D (1999) Hybrid system approach to optimum design of a ship. AI EDAM 13: pp. 111 CrossRef
 Lewis, K (2002) Multidisciplinary design optimization. Aerosp Am 40: pp. 42
 Merkle, D, Middendorf, M (2002) Ant colony optimization for resourceconstrained project scheduling. IEEE Trans Evol Comput 6: pp. 333346 CrossRef
 Michelena, N, Park, H (2003) Convergence properties of analytical target cascading. AIAA J 41: pp. 897905 CrossRef
 Mistree, F, Smith, WF (1990) Decisionbased design: a contemporary paradigm for ship design. Trans Soc Naval Arch Marine Eng 98: pp. 565597
 Moles, CG, Mendes, P (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13: pp. 24672474 CrossRef
 Moraes, HB, Vasconcellos, JM (2007) Multiple criteria optimization applied to high speed catamaran preliminary design. Ocean Eng 34: pp. 133147 CrossRef
 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, Italy
 Papalambros, PY, Wilde, DJ (2000) Principles of optimal design. Cambridge University Press, Cambridge CrossRef
 Parashar S, Bloebaum CL (2006) Multiobjective genetic algorithm concurrent subspace optimization (MOGACSSO) for multidisciplinary design. In: 47th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, pp 1–11
 Parsons, MG, Scott, RL (2004) Formulation of multicriterion design optimization problems for solution with scalar numerical optimization methods. J Ship Res 48: pp. 6176
 Parsons MG, Singer DJ et al (1999) A hybrid agent approach for setbased conceptual ship design. In: Proceedings of the international conference on computer applications in shipbuilding, Cambridge, MA, pp 7–11
 Peri D, Campana EF (2003a) High fidelity models in the multidisciplinary optimization of a frigate ship. In: 2nd MIT conference on fluid and solid mechanics, Cambridge
 Peri, D, Campana, EF (2003) Multidisciplinary design optimization of a naval surface combatant. J Ship Res 47: pp. 112
 Peri, D, Campana, EF (2005) Highfidelity models for multiobjective global optimization in simulationbased design. J Ship Res 49: pp. 159175
 Peri D, Campana EF et al (2001a) Development of CFDbased design optimization architecture. In: 1st MIT conference on fluid and solid mechanics, Cambridge
 Peri, D, Rossetti, M (2001) Design optimization of ship hulls via CFD techniques. J Ship Res 45: pp. 140149
 Pinto, A, Peri, D (2007) Multiobjective optimization of a containership using deterministic particle swarm optimization. J Ship Res 51: pp. 217228
 Poli R, Kennedy J et al (2008) Editorial: particle swarms: the second decade. J Artif Evol Appl 2008(3)
 Ray, T, Gokarn, RP (1995) A global optimization model for ship design. Comput Ind 26: pp. 175192 CrossRef
 Schutte, J, Reinbolt, J (2004) Parallel global optimization with the particle swarm algorithm. Int J Numer Meth Eng 61: pp. 22962315 CrossRef
 Sen, P, Yang, JB (1998) Multiple criteria decision support in engineering design. Springer, New York
 Serra, M, Venini, P (2006) On some applications of ant colony optimization metaheuristic to plane truss optimization. Struct Multidisc Optim 32: pp. 499506 CrossRef
 Shi, Y, Eberhart, RC (1998) Parameter selection in particle swarm optimization. Evol Program 7: pp. 611616
 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 3
 Shi Y, Eberhart RC et al (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the 2001 congress on evolutionary computation, vol 1
 Sinha, K (2007) Reliabilitybased multiobjective optimization for automotive crashworthiness and occupant safety. Struct Multidisc Optim 33: pp. 255268 CrossRef
 Statnikov, RB, Matusov, JB (1995) Multicriteria optimization and engineering. Chapman & Hall, New York
 Sun J, Zhang G et al (2006) Multidisciplinary design optimization under uncertainty for thermal protection system applications. AAIA Paper 20067002547
 Tahara, Y, Tohyama, S (2006) CFDbased multiobjective optimization method for ship design. Int J Numer Methods Fluids 52: pp. 499 CrossRef
 Tosserams, S, Etman, LFP (2006) An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers. Struct Multidisc Optim 31: pp. 176189 CrossRef
 Bergh, F, Engelbrecht, A (2006) A study of particle swarm optimization particle trajectories. Inf Moksl 176: pp. 937971
 Venkayya, VB (1989) Optimality criteria: a basis for multidisciplinary design optimization. Comput Mech 5: pp. 121 CrossRef
 Venter, G, SobieszczanskiSobieski, J (2003) Particle swarm optimization. AIAA J 41: pp. 15831589 CrossRef
 Venter, G, SobieszczanskiSobieski, J (2004) Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Struct Multidisc Optim 26: pp. 121131 CrossRef
 Venter, G, SobieszczankiSobieksi, J (2006) A parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J Aero Comput Inform Commun 3: pp. 123137 CrossRef
 Vianese J (2004) Multidisciplinary optimization of naval ship design and mission effectiveness, vol 85. Defense Technical Information Center, US Dept of Defense, Washington, DC
 Villagra, M, Baran, B (2007) Ant colony optimization with adaptive fitness function for satisfiability testing. Lect Notes Comput Sci 4576: pp. 352 CrossRef
 Vlahopoulos N, Wang A et al (2008) Engaging structuralacoustic simulations in multidiscipline optimization. In: NOISECON 2008, Dearborn, MI
 Wang, J, Yin, Z (2008) A ranking selectionbased particle swarm optimizer for engineering design optimization problems. Struct Multidisc Optim 37: pp. 131147 CrossRef
 Yang, YS, Park, CK (2007) A study on the preliminary ship design method using deterministic approach and probabilistic approach including hull form. Struct Multidisc Optim 33: pp. 529539 CrossRef
 Yi, SI, Shin, JK (2008) Comparison of MDO methods with mathematical examples. Struct Multidisc Optim 35: pp. 391402 CrossRef
 Title
 An integrated multidisciplinary particle swarm optimization approach to conceptual ship design
 Journal

Structural and Multidisciplinary Optimization
Volume 41, Issue 3 , pp 481494
 Cover Date
 20100401
 DOI
 10.1007/s0015800904140
 Print ISSN
 1615147X
 Online ISSN
 16151488
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Metaheuristic
 Particle swarm optimization
 Multidisciplinary design optimization
 Ship design
 Industry Sectors
 Authors

 Christopher G. Hart ^{(1)}
 Nickolas Vlahopoulos ^{(2)}
 Author Affiliations

 1. Naval Architecture and Marine Engineering Department, Stephen M. Ross School of Business, Ann Arbor, MI, USA
 2. Naval Architecture and Marine Engineering Department, Mechanical Engineering Department, College of Engineering, University of Michigan, Ann Arbor, MI, 48105, USA