[2013] Applications of Vector Evaluated Genetic Algorithms (VEGA) in Ultimate Limit State Based Ship Structural Design > Selected Papers

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Selected Papers

[2013] Applications of Vector Evaluated Genetic Algorithms (VEGA) in U…

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Proceedings of OMAE2014
The 33th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2014), 8-13 June 2013, San Francisco, California, USA.


Applications of Vector Evaluated Genetic Algorithms (VEGA) in Ultimate Limit State Based Ship Structural Design

Author(s): Owen Hughes, Ming Ma, Jeom Kee Paik

Abstract:
Ship structural design often deals with multiple objectives such as weight, safety, and cost. These objectives usually conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to these problems. In this paper a multi- objective GA, called Vector Evaluated Genetic Algorithm (VEGA) is formulated and used to optimize a large and complex thin-wall structure (a complete cargo hold of a 200,000 ton oil tanker) on the basis of weight, safety and cost. The structure weight and cost and all of the stresses are calculated using a realistic finite element model. The structure adequacy is then evaluated using the ALPS/ULSAP computer program (Paik and Thayamballi, 2003) which can efficiently evaluate all six ultimate limit states for stiffened panels and grillages. This example was chosen because the initial design is severely inadequate.  The results show that the proposed method can perform ultimate strength based structural optimization with multi-objectives, namely minimization of the structural weight and cost and maximization of structural safety, and also that the method is very robust.

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