Reducing uncertainty in subdivision optimization

Romanas Puisa, Nikolaos Tsakalakis, Dracos Vassalos

Research output: Contribution to journalArticle

Abstract

Design of watertight subdivision inherently involves its optimization with the objective to increase the index “A” above its minimum required value. In view of a big popularity of probabilistic search methods such as genetic algorithms, this task is intrinsically time consuming. Thus, even when an optimal subdivision layout (i.e. topology) is determined, it can be found that the optimal bulkhead positions can be a great challenge time-wise, often forcing designers to satisfy with suboptimal solutions. The fundamental reason why this happens is that the nature of the optimized function (e.g., index “A” as a function of bulkhead positions) is unknown and hence it has no effect upon the choice of optimization strategy, which therefore reflects subjective but not factual preferences. In this paper we study the nature of functional dependency between the subdivision index and bulkhead positions, as a simplest case, and indicate pertinent optimization strategies that consequently reduce the optimization time. In our study we use a cruise ship model to demonstrate the application results of our findings.
LanguageEnglish
Pages18-27
Number of pages10
JournalJournal of Shipping and Ocean Engineering
Volume2
Publication statusPublished - 2012

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Ship models
Genetic algorithms
Topology
Uncertainty

Keywords

  • damage stability
  • optimization
  • watertight subdivision
  • index A
  • logistic regression
  • cruise ship
  • approximation
  • surrogate function

Cite this

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Reducing uncertainty in subdivision optimization. / Puisa, Romanas; Tsakalakis, Nikolaos; Vassalos, Dracos.

In: Journal of Shipping and Ocean Engineering, Vol. 2, 2012, p. 18-27.

Research output: Contribution to journalArticle

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AU - Puisa, Romanas

AU - Tsakalakis, Nikolaos

AU - Vassalos, Dracos

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AB - Design of watertight subdivision inherently involves its optimization with the objective to increase the index “A” above its minimum required value. In view of a big popularity of probabilistic search methods such as genetic algorithms, this task is intrinsically time consuming. Thus, even when an optimal subdivision layout (i.e. topology) is determined, it can be found that the optimal bulkhead positions can be a great challenge time-wise, often forcing designers to satisfy with suboptimal solutions. The fundamental reason why this happens is that the nature of the optimized function (e.g., index “A” as a function of bulkhead positions) is unknown and hence it has no effect upon the choice of optimization strategy, which therefore reflects subjective but not factual preferences. In this paper we study the nature of functional dependency between the subdivision index and bulkhead positions, as a simplest case, and indicate pertinent optimization strategies that consequently reduce the optimization time. In our study we use a cruise ship model to demonstrate the application results of our findings.

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KW - approximation

KW - surrogate function

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