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http://dx.doi.org/10.5050/KSNVN.2006.16.6.665

Development of the New Hybrid Evolutionary Algorithm for Low Vibration of Ship Structures  

Kong, Young-Mo (대우조선해양(주) 진동소음연구팀)
Choi, Su-Hyun (대우조선해양(주) 진동소음연구팀)
Song, Jin-Dae (부경대학교 기계공학부)
Yang, Bo-Suk (부경대학교 기계공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.16, no.6, 2006 , pp. 665-673 More about this Journal
Abstract
This paper proposes a RSM-based hybrid evolutionary Algorithm (RHEA) which combines the merits of the popular programs such as genetic algorithm (GA), tabu search method and response surface methodology (RSM). This algorithm, for improving the convergent speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation of GA offers random variety to finding the optimum solution. In this study, however, systematic variety can be secured through the use of tabu list. Efficiency of this method has been proven by applying traditional left functions and comparing the results to GA. It was also proved that the newly suggested algorithm is very effective to find the global optimum solution to minimize the weight for avoiding the resonance of fresh water tank that is placed in the after body area of ship. According to the study, GA's convergent speed in initial stages is improved by using RSM method. An optimized solution is calculated without the evaluation of additional actual objective function. In a summary, it is concluded that RHEA is a very powerful global optimization algorithm from the view point of convergent speed and global search ability.
Keywords
Genetic Algorithm; Response Surface Methodology; Tabu Search Method;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Yang, B. S., Kong, Y. M., Choi, S. H., Chae, S. I., Song, S. D. and Kim, Y. H., 2005, 'Development of NASTRAN-based Optimization Framework for Vibration Optimum Design of Ship Structure,' Transactions of the Korean Society for Noise and Vibration Engineering, Vol. 15, No. 11, pp. 1223 - 1231   과학기술학회마을   DOI   ScienceOn
2 Davis, L., 1991, 'Handbook of Genetic Algorithms,' Van Nostrand Reinhold, New York, pp, 3 - 23
3 Sato, T. and Hagiwara, M., 1998, 'Bee System: Finding Solution by a Concentrated Search,' T. lEE Japan, Vol. 118-C, No.5, pp.721 -726
4 Howard, D., Mark, B. and Martin, H., 2005, 'Neural Network Toolbox for Use with MATLAB, ' The MathWorks
5 Kim, Y. C., 2003, 'Development of Enhanced Genetic Algorithm and Its Applications to Optimum Design of Rotating Machinery,' Ph. D. Dissertation, Pukyong National University, South Korea
6 Homaifar, A., Qi, C. and Lai, S., 1994, 'Constrained Optimization via Genetic Algorithm Simulation. Electronics Letter,' Vol. 62, No.4, pp. 242 - 254
7 Kim, Y. C. and Yang, B. S., 2002, 'An Enhanced Genetic Algorithm for Global and Local Optimization Search,' Transactions of the KSME (A) Vol. 26, No.5, pp. 1008 - 1015   과학기술학회마을   DOI   ScienceOn
8 Kim, Y. H., Yang, B. S., Kim, Y. C. and Lee, S. J., 2003, 'Bearing Parameters Identification Using Hybrid Optimization Algorithm,' in Proceedings of 32nd International Congress and Exposition on Noise Control Engineering, Jeiu, Korea, pp. 4212 - 4219
9 Kitamura, M, Nobukawa, H. and Yang, F., 2000, 'Application of a Genetic Algorithm to the Optimal Structural Design of a Ship's Engine Room Taking Dynamic Constraints into Consideration,' Marine Science and Technology, Vol. 5, pp. 131-146   DOI
10 Goldberg, D. E., 1989, 'Genetic Algorithms in . Search, Optimization & Machine Learning,' Addison-Wesley Publishing Company, pp. 1 -146