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Genetic optimization for yacht design

Published: 07 July 2007 Publication History

Abstract

This paper introduces a procedure for using genetic multi-objective optimization in yacht design. The problem described consists on the optimization of a bulb shape to improve the performance of the yacht. The two objectives considered are the minimization of the drag in calm water together with the minimization of the Vertical Center of Gravity (VCG), all the configurations should satisfy length and volume constraints. Since there is no a single optimum to be found, the MOGA-II was used as multi-objective genetic algorithm. The distributed optimization search exploited the parallelization capabilities of the MOGA-II algorithm which allowed the evaluation of several designs configurations by running concurrent threads of the flow analysis solver.
Three bulb shapes of different length are selected between the non-dominated solutions. Using these three solutions, seakeeping tests of a fully appended scale model have been carried out at the towing tank of the University of Trieste. A single hull has been tested for each bulb configurations to check the influence of the bulb shape on the performance of the yacht in waves.
The results obtained are very satisfactory, and the procedure described can be applied to even more complex yacht design problems.

References

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ICEMCFD 10.0, ANSYS Inc., ICEMCFD User Manual
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modeFRONTIER 3.2.0, ESTECO srl, modeFRONTIER User Manual
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www.puertos.es/externo/clima/Rayo/rvaledes.html.
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X. Yang and M. Hayes "Application of Grid techniques in the CFD field". Proceedings of Integrating CFD and Experiments in Aerodynamics, Glasgow, September 2003
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S. Poles, "NBI and MOGA-II, two complementary algorithm for Molti-Objective optimization". Dagstuhl Seminar Proceedings 04461, Practical Approaches to Multi-Objective Optimization, http://drops.dagstuhl.de/opus/volltexte/2005/272.
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K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. "A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. "Proceedings of the Parallel Problem Solving from Nature VI Conference, Springer. Lecture Notes in Computer Science No. 1917, Paris, France, pp. 849--858, 2000
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Poloni, C. and Pediroda, V. GA coupled with computationally expensive simulations: tools to improve efficiency. Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, pages 267--288, John Wiley and Sons, England, 1997.
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Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading Mass, USA
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I. M. Sobol "On the Systematic Search in a Hypercube" SIAM Journal on Numerical Analysis, Vol. 16, No. 5 (Oct., 1979), pp. 790--793.
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S. Poles, Y. Fu, E. Rigoni "The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms". MOPGP June 2006 - Loire Valley, France.

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  • (2011)Manufacturing Process Simulation for Product Design Chain OptimizationMaterials and Manufacturing Processes10.1080/10426914.2011.56424826:3(527-533)Online publication date: 11-Apr-2011

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
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Publication History

Published: 07 July 2007

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  1. multiobjective optimization
  2. yacht design

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2011)Manufacturing Process Simulation for Product Design Chain OptimizationMaterials and Manufacturing Processes10.1080/10426914.2011.56424826:3(527-533)Online publication date: 11-Apr-2011

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