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Genetic optimization for yacht design
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents
London, England
SESSION: Real-world applications: papers table of contents
Pages: 2007 - 2012  
Year of Publication: 2007
ISBN:978-1-59593-697-4
Authors
Paolo Geremia  ESTECO srl
Mauro Poian  ESTECO srl
Silvia Poles  ESTECO srl
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
CFX 10.0, ANSYS Inc., CFX User Manual
 
2
ICEMCFD 10.0, ANSYS Inc., ICEMCFD User Manual
 
3
modeFRONTIER 3.2.0, ESTECO srl, modeFRONTIER User Manual
 
4
www.puertos.es/externo/clima/Rayo/rvaledes.html.
 
5
X. Yang and M. Hayes "Application of Grid techniques in the CFD field". Proceedings of Integrating CFD and Experiments in Aerodynamics, Glasgow, September 2003
 
6
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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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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I. M. Sobol "On the Systematic Search in a Hypercube" SIAM Journal on Numerical Analysis, Vol. 16, No. 5 (Oct., 1979) , pp. 790--793.
 
11
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.

Collaborative Colleagues:
Paolo Geremia: colleagues
Mauro Poian: colleagues
Silvia Poles: colleagues